Fertility prediction from wearable-based physiological data

ABSTRACT

Methods, systems, and devices for fertility prediction are described. A system may be configured to receive physiological data collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values taken over the plurality of days where the time series includes a plurality of menstrual cycles. The system may then determine a plurality of menstrual cycle length parameters associated with the menstrual cycles and determine a user fertility prediction based on the menstrual cycle length parameters. The system may generate a message for display on a graphical user interface that indicates the determined user fertility prediction.

CROSS REFERENCE

The present Application for Patent claims the benefit of U.S.Provisional Patent Application No. 63/169,314 by Aschbacher et al.,entitled “WOMEN'S HEALTH TRACKING,” filed Apr. 1, 2021, assigned to theassignee hereof, and expressly incorporated by reference herein.

FIELD OF TECHNOLOGY

The following relates to wearable devices and data processing, includingfertility prediction from wearable-based physiological data.

BACKGROUND

Some wearable devices may be configured to collect data from usersassociated with body temperature and heart rate. For example, somewearable devices may be configured to detect cycles associated withreproductive health. However, conventional cycle detection techniquesimplemented by wearable devices are deficient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a system that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure.

FIG. 2 illustrates an example of a system that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure.

FIG. 3 illustrates an example of a system that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure.

FIG. 4 illustrates examples of timing diagrams that support fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure.

FIG. 5 illustrates an example of a timing diagram that supportsfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure.

FIG. 6 illustrates an example of a graphical user interface (GUI) thatsupports fertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure.

FIG. 7 shows a block diagram of an apparatus that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure.

FIG. 8 shows a block diagram of a wearable application that supportsfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure.

FIG. 9 shows a diagram of a system including a device that supportsfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure.

FIGS. 10 through 12 show flowcharts illustrating methods that supportfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure.

DETAILED DESCRIPTION

Some wearable devices may be configured to collect physiological datafrom users, including temperature data, heart rate data, and the like.Acquired physiological data may be used to analyze the user's movementand other activities, such as sleeping patterns. Many users have adesire for more insight regarding their physical health, including theirsleeping patterns, activity, and overall physical well-being. Inparticular, many users may have a desire for more insight regardingwomen's health, including their menstrual cycle, ovulation, fertilitypatterns, and pregnancy. However, typical cycle tracking or women'shealth devices and applications lack the ability to provide robustprediction, detection, and insight for several reasons.

First, typical cycle prediction applications require users to manuallytake their temperature with a device at a discrete time each day. Thissingle temperature data point may not provide sufficient context toaccurately capture or predict the true temperature variations indicativeof woman's health cycle patterns, and may be difficult to accuratelycapture given the sensitivity of the measuring device to user movementor exertion. Second, even for devices that are wearable or that take auser's temperature more frequently throughout the day, typical devicesand applications lack the ability to collect other physiological,behavioral, or contextual inputs from the user that can be combined withthe measured temperature to more comprehensively understand the completeset of physiological contributors to a women's cycle.

Aspects of the present disclosure are directed to techniques forfertility prediction and/or assessment. In particular, computing devicesof the present disclosure may receive physiological data includingtemperature data from the wearable device associated with the user anddetermine a time series of temperature values taken over a plurality ofdays. The time series may include a plurality of menstrual cycles forthe user. For example, aspects of the present disclosure may determine aplurality of menstrual cycle length parameters associated with theplurality of menstrual cycles, such as average menstrual cycle length, astandard deviation menstrual cycle length, an average follicular phaselength, an average luteal phase length, a range of menstrual cyclelengths, a quantity of anovulatory cycles, or a combination thereof. Assuch, aspects of the present disclosure may determine a user fertilityprediction based on the plurality of menstrual cycle length parameters.

In some implementations, the system may analyze historical temperaturedata from a user, determine the plurality of menstrual cycle lengthparameters, determine the user fertility prediction, and generate anindication to a user that indicates the user's determined fertilityprediction. The system may also analyze temperature series data in realtime and may determine the menstrual cycle length parameters and userfertility prediction in real time based on determining the time seriesof the temperature data and/or based on the user's input.

For the purposes of the present disclosure, the term “fertilityprediction,” “user fertility prediction,” “fertility score,” and thelike may be used to refer to a calculation or assessment by a systemdescribed herein of a user's capability to conceive or induce conceptionduring which one or more offspring develops inside the womb. During aportion of the menstrual cycle, the user may be able to become pregnantbefore and during ovulation. If a user is able to conceive (e.g., becomepregnant), the user may be referred to as fertile. If a user is unableto conceive after a predetermined amount of time, the user may bereferred to as infertile, and the user may be experiencing infertilityissues. In some cases, ovulation dates may correspond to a fertilitywindow in which the user may become pregnant. The fertility score mayrefer to a numerical value that includes fertility-related contributorsthat enable a user to track their fertility longitudinally acrossreproductive lifespans.

In some cases, a user's fertility may be based on one or more fertilitycontributors. The fertility contributors may include one or morephysiological measurements, ovarian reserve contributors, menstrualcycle length contributors, or a combination thereof. The one or morephysiological measurements may include temperature data, respiratoryrate data, heart rate data, heart rate variability (HRV), or acombination thereof, as described herein. For example, increasedrespiratory rate, increased heart rate, and decreased HRV mayindependently or in combination indicate declining fertility. In otherexamples, decreased respiratory rate, decreased heart rate, andincreased HRV may independently or in combination indicate increasingfertility.

In some examples, the ovarian reserve contributors may include a user'shormone levels of follicle-stimulating hormone (FSH), (anti-müllerianhormone (AMH), or both, antral follicle count (AFC), or a combinationthereof. In such cases, the system may determine the user fertilityprediction based on FSH levels, AFC levels, and AFC levels. For example,FSH levels, AFC levels, and AFC levels may be reflected in cardiac andrespiratory metrics (e.g., heart rate, HRV, respiratory rate, or acombination thereof).

The menstrual cycle length contributors may include a menstrual cyclelength (e.g., including a follicular phase length, a luteal phaselength, or both), a cycle regularity, or both. In some cases, the systemmay determine the user fertility prediction based on the length of themenstrual cycle. For example, a longer menstrual cycle length mayindicate that ovulation is not occurring, fertility related issues, orboth. A shorter menstrual cycle length may indicate a missed ovulatoryevent, a luteal phase defect, or both. For example, menstrual cycles oftwenty six days or less may be associated with reduced fecundability(e.g., the probability of achieving a pregnancy within one menstrualcycle). In such cases, the system may determine the user fertilityprediction based on one or more physiological parameters, ovarianreserve contributors, menstrual cycle length contributors, or acombination thereof.

Some aspects of the present disclosure are directed to predicting auser's fertility before the user tries to conceive (e.g., who plans tobecome pregnant in the future). However, techniques described herein mayalso be used to predict the user's fertility in cases where the user iscurrently and actively trying to become pregnant now. In someimplementations, the computing devices may determine the plurality ofmenstrual cycle length parameters and determine the user fertilityprediction using a temperature sensor. In such cases, the computingdevices may determine the user fertility prediction without the usertagging or labeling events.

In conventional systems, fertility may be assessed by a clinicianspecializing in fertility and may require the use of blood tests,questionnaires, ultrasounds, and/or other forms of physical examinationor tests. Techniques described herein may continuously collectphysiological data from the user based on measurements taken from awearable that continuously measures a user's surface temperature andsignals extracted from blood flow such as arterial blood flow (e.g., viaPPG). In some implementations, the computing devices may sample theuser's temperature continuously throughout the day and night. Samplingat a sufficient rate (e.g., one sample per minute) throughout the dayand night (or at certain phases of the day and/or during certain phasesof a sleep cycle) may provide sufficient temperature data for analysisdescribed herein.

In some cases, continuous temperature measurement at the finger maycapture temperature fluctuations (e.g., small or large fluctuations)that may not be evident in core temperature. For example, continuoustemperature measurement at the finger may capture minute-to-minute orhour-to-hour temperature fluctuations that provide additional insightthat may not be provided by other temperature measurements elsewhere inthe body or if the user were manually taking their temperature once perday. As such, data collected by the computing devices may be used todetermine the user's fertility prediction.

Techniques described herein may notify a user of the determinedfertility prediction in a variety of ways. For example, a system maycause a graphical user interface (GUI) of a user device to display amessage or other notification to notify the user of the determinedfertility prediction and make recommendations to the user. In oneexample, the GUI may display alerts to a user when early signs ofinfertility are detected. In some implementations, the system may maketag recommendations to a user. For example, the system may indicate atime interval during which the determined fertility prediction is validfor at determined times in their menstrual cycles (e.g., in apersonalized manner). The system may determine the user fertilityprediction based on their prior history of temperature data,personalized cycling patterns, and/or their prior medical history.

The system may also include graphics or text that indicate the data usedto make the determination of the fertility prediction. For example, theGUI may display an indication of physiological and behavioral indicatorsthat contribute to the determined user fertility prediction. In somecases, the GUI may display a probability of becoming pregnant within thetime interval based on the determined user fertility prediction. Basedon the early detection (e.g., before the user experiences difficultiesin becoming pregnant), a user may take early steps that may help reducethe difficulties in becoming pregnant, increase fertility, and improvethe determined user fertility prediction. Additionally, a user maymodify/schedule their daily activities (e.g., work and leisure time)based on the determined fertility prediction.

Aspects of the disclosure are initially described in the context ofsystems supporting physiological data collection from users via wearabledevices. Additional aspects of the disclosure are described in thecontext of example timing diagrams and example GUIs. Aspects of thedisclosure are further illustrated by and described with reference toapparatus diagrams, system diagrams, and flowcharts that relate tofertility prediction from wearable-based physiological data.

FIG. 1 illustrates an example of a system 100 that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure. The system 100 includes a pluralityof electronic devices (e.g., wearable devices 104, user devices 106)that may be worn and/or operated by one or more users 102. The system100 further includes a network 108 and one or more servers 110.

The electronic devices may include any electronic devices known in theart, including wearable devices 104 (e.g., ring wearable devices, watchwearable devices, etc.), user devices 106 (e.g., smartphones, laptops,tablets). The electronic devices associated with the respective users102 may include one or more of the following functionalities: 1)measuring physiological data, 2) storing the measured data, 3)processing the data, 4) providing outputs (e.g., via GUIs) to a user 102based on the processed data, and 5) communicating data with one anotherand/or other computing devices. Different electronic devices may performone or more of the functionalities.

Example wearable devices 104 may include wearable computing devices,such as a ring computing device (hereinafter “ring”) configured to beworn on a user's 102 finger, a wrist computing device (e.g., a smartwatch, fitness band, or bracelet) configured to be worn on a user's 102wrist, and/or a head mounted computing device (e.g., glasses/goggles).Wearable devices 104 may also include bands, straps (e.g., flexible orinflexible bands or straps), stick-on sensors, and the like, that may bepositioned in other locations, such as bands around the head (e.g., aforehead headband), arm (e.g., a forearm band and/or bicep band), and/orleg (e.g., a thigh or calf band), behind the ear, under the armpit, andthe like. Wearable devices 104 may also be attached to, or included in,articles of clothing. For example, wearable devices 104 may be includedin pockets and/or pouches on clothing. As another example, wearabledevice 104 may be clipped and/or pinned to clothing, or may otherwise bemaintained within the vicinity of the user 102. Example articles ofclothing may include, but are not limited to, hats, shirts, gloves,pants, socks, outerwear (e.g., jackets), and undergarments. In someimplementations, wearable devices 104 may be included with other typesof devices such as training/sporting devices that are used duringphysical activity. For example, wearable devices 104 may be attached to,or included in, a bicycle, skis, a tennis racket, a golf club, and/ortraining weights.

Much of the present disclosure may be described in the context of a ringwearable device 104. Accordingly, the terms “ring 104,” “wearable device104,” and like terms, may be used interchangeably, unless notedotherwise herein. However, the use of the term “ring 104” is not to beregarded as limiting, as it is contemplated herein that aspects of thepresent disclosure may be performed using other wearable devices (e.g.,watch wearable devices, necklace wearable device, bracelet wearabledevices, earring wearable devices, anklet wearable devices, and thelike).

In some aspects, user devices 106 may include handheld mobile computingdevices, such as smartphones and tablet computing devices. User devices106 may also include personal computers, such as laptop and desktopcomputing devices. Other example user devices 106 may include servercomputing devices that may communicate with other electronic devices(e.g., via the Internet). In some implementations, computing devices mayinclude medical devices, such as external wearable computing devices(e.g., Holter monitors). Medical devices may also include implantablemedical devices, such as pacemakers and cardioverter defibrillators.Other example user devices 106 may include home computing devices, suchas internet of things (IoT) devices (e.g., IoT devices), smarttelevisions, smart speakers, smart displays (e.g., video call displays),hubs (e.g., wireless communication hubs), security systems, smartappliances (e.g., thermostats and refrigerators), and fitness equipment.

Some electronic devices (e.g., wearable devices 104, user devices 106)may measure physiological parameters of respective users 102, such asphotoplethysmography waveforms, continuous skin temperature, a pulsewaveform, respiration rate, heart rate, HRV, actigraphy, galvanic skinresponse, pulse oximetry, and/or other physiological parameters. Someelectronic devices that measure physiological parameters may alsoperform some/all of the calculations described herein. Some electronicdevices may not measure physiological parameters, but may performsome/all of the calculations described herein. For example, a ring(e.g., wearable device 104), mobile device application, or a servercomputing device may process received physiological data that wasmeasured by other devices.

In some implementations, a user 102 may operate, or may be associatedwith, multiple electronic devices, some of which may measurephysiological parameters and some of which may process the measuredphysiological parameters. In some implementations, a user 102 may have aring (e.g., wearable device 104) that measures physiological parameters.The user 102 may also have, or be associated with, a user device 106(e.g., mobile device, smartphone), where the wearable device 104 and theuser device 106 are communicatively coupled to one another. In somecases, the user device 106 may receive data from the wearable device 104and perform some/all of the calculations described herein. In someimplementations, the user device 106 may also measure physiologicalparameters described herein, such as motion/activity parameters.

For example, as illustrated in FIG. 1, a first user 102-a (User 1) mayoperate, or may be associated with, a wearable device 104-a (e.g., ring104-a) and a user device 106-a that may operate as described herein. Inthis example, the user device 106-a associated with user 102-a mayprocess/store physiological parameters measured by the ring 104-a.Comparatively, a second user 102-b (User 2) may be associated with aring 104-b, a watch wearable device 104-c (e.g., watch 104-c), and auser device 106-b, where the user device 106-b associated with user102-b may process/store physiological parameters measured by the ring104-b and/or the watch 104-c. Moreover, an nth user 102-n (User N) maybe associated with an arrangement of electronic devices described herein(e.g., ring 104-n, user device 106-n). In some aspects, wearable devices104 (e.g., rings 104, watches 104) and other electronic devices may becommunicatively coupled to the user devices 106 of the respective users102 via Bluetooth, Wi-Fi, and other wireless protocols.

In some implementations, the rings 104 (e.g., wearable devices 104) ofthe system 100 may be configured to collect physiological data from therespective users 102 based on arterial blood flow within the user'sfinger. In particular, a ring 104 may utilize one or more LEDs (e.g.,red LEDs, green LEDs) that emit light on the palm-side of a user'sfinger to collect physiological data based on arterial blood flow withinthe user's finger. In some implementations, the ring 104 may acquire thephysiological data using a combination of both green and red LEDs. Thephysiological data may include any physiological data known in the artincluding, but not limited to, temperature data, accelerometer data(e.g., movement/motion data), heart rate data, HRV data, blood oxygenlevel data, or any combination thereof.

The use of both green and red LEDs may provide several advantages overother solutions, as red and green LEDs have been found to have their owndistinct advantages when acquiring physiological data under differentconditions (e.g., light/dark, active/inactive) and via different partsof the body, and the like. For example, green LEDs have been found toexhibit better performance during exercise. Moreover, using multipleLEDs (e.g., green and red LEDs) distributed around the ring 104 has beenfound to exhibit superior performance as compared to wearable devicesthat utilize LEDs that are positioned close to one another, such aswithin a watch wearable device. Furthermore, the blood vessels in thefinger (e.g., arteries, capillaries) are more accessible via LEDs ascompared to blood vessels in the wrist. In particular, arteries in thewrist are positioned on the bottom of the wrist (e.g., palm-side of thewrist), meaning only capillaries are accessible on the top of the wrist(e.g., back of hand side of the wrist), where wearable watch devices andsimilar devices are typically worn. As such, utilizing LEDs and othersensors within a ring 104 has been found to exhibit superior performanceas compared to wearable devices worn on the wrist, as the ring 104 mayhave greater access to arteries (as compared to capillaries), therebyresulting in stronger signals and more valuable physiological data.

The electronic devices of the system 100 (e.g., user devices 106,wearable devices 104) may be communicatively coupled to one or moreservers 110 via wired or wireless communication protocols. For example,as shown in FIG. 1, the electronic devices (e.g., user devices 106) maybe communicatively coupled to one or more servers 110 via a network 108.The network 108 may implement transfer control protocol and internetprotocol (TCP/IP), such as the Internet, or may implement other network108 protocols. Network connections between the network 108 and therespective electronic devices may facilitate transport of data viaemail, web, text messages, mail, or any other appropriate form ofinteraction within a computer network 108. For example, in someimplementations, the ring 104-a associated with the first user 102-a maybe communicatively coupled to the user device 106-a, where the userdevice 106-a is communicatively coupled to the servers 110 via thenetwork 108. In additional or alternative cases, wearable devices 104(e.g., rings 104, watches 104) may be directly communicatively coupledto the network 108.

The system 100 may offer an on-demand database service between the userdevices 106 and the one or more servers 110. In some cases, the servers110 may receive data from the user devices 106 via the network 108, andmay store and analyze the data. Similarly, the servers 110 may providedata to the user devices 106 via the network 108. In some cases, theservers 110 may be located at one or more data centers. The servers 110may be used for data storage, management, and processing. In someimplementations, the servers 110 may provide a web-based interface tothe user device 106 via web browsers.

In some aspects, the system 100 may detect periods of time during whicha user 102 is asleep, and classify periods of time during which the user102 is asleep into one or more sleep stages (e.g., sleep stageclassification). For example, as shown in FIG. 1, User 102-a may beassociated with a wearable device 104-a (e.g., ring 104-a) and a userdevice 106-a. In this example, the ring 104-a may collect physiologicaldata associated with the user 102-a, including temperature, heart rate,HRV, respiratory rate, and the like. In some aspects, data collected bythe ring 104-a may be input to a machine learning classifier, where themachine learning classifier is configured to determine periods of timeduring which the user 102-a is (or was) asleep. Moreover, the machinelearning classifier may be configured to classify periods of time intodifferent sleep stages, including an awake sleep stage, a rapid eyemovement (REM) sleep stage, a light sleep stage (non-REM (NREM)), and adeep sleep stage (NREM). In some aspects, the classified sleep stagesmay be displayed to the user 102-a via a GUI of the user device 106-a.Sleep stage classification may be used to provide feedback to a user102-a regarding the user's sleeping patterns, such as recommendedbedtimes, recommended wake-up times, and the like. Moreover, in someimplementations, sleep stage classification techniques described hereinmay be used to calculate scores for the respective user, such as SleepScores, Readiness Scores, and the like.

In some aspects, the system 100 may utilize circadian rhythm-derivedfeatures to further improve physiological data collection, dataprocessing procedures, and other techniques described herein. The termcircadian rhythm may refer to a natural, internal process that regulatesan individual's sleep-wake cycle, that repeats approximately every 24hours. In this regard, techniques described herein may utilize circadianrhythm adjustment models to improve physiological data collection,analysis, and data processing. For example, a circadian rhythmadjustment model may be input into a machine learning classifier alongwith physiological data collected from the user 102-a via the wearabledevice 104-a. In this example, the circadian rhythm adjustment model maybe configured to “weight,” or adjust, physiological data collectedthroughout a user's natural, approximately 24-hour circadian rhythm. Insome implementations, the system may initially start with a “baseline”circadian rhythm adjustment model, and may modify the baseline modelusing physiological data collected from each user 102 to generatetailored, individualized circadian rhythm adjustment models that arespecific to each respective user 102.

In some aspects, the system 100 may utilize other biological rhythms tofurther improve physiological data collection, analysis, and processingby phase of these other rhythms. For example, if a weekly rhythm isdetected within an individual's baseline data, then the model may beconfigured to adjust “weights” of data by day of the week. Biologicalrhythms that may require adjustment to the model by this methodinclude: 1) ultradian (faster than a day rhythms, including sleep cyclesin a sleep state, and oscillations from less than an hour to severalhours periodicity in the measured physiological variables during wakestate; 2) circadian rhythms; 3) non-endogenous daily rhythms shown to beimposed on top of circadian rhythms, as in work schedules; 4) weeklyrhythms, or other artificial time periodicities exogenously imposed(e.g. in a hypothetical culture with 12 day “weeks”, 12 day rhythmscould be used); 5) multi-day ovarian rhythms in women andspermatogenesis rhythms in men; 6) lunar rhythms (relevant forindividuals living with low or no artificial lights); and 7) seasonalrhythms.

The biological rhythms are not always stationary rhythms. For example,many women experience variability in ovarian cycle length across cycles,and ultradian rhythms are not expected to occur at exactly the same timeor periodicity across days even within a user. As such, signalprocessing techniques sufficient to quantify the frequency compositionwhile preserving temporal resolution of these rhythms in physiologicaldata may be used to improve detection of these rhythms, to assign phaseof each rhythm to each moment in time measured, and to thereby modifyadjustment models and comparisons of time intervals. The biologicalrhythm-adjustment models and parameters can be added in linear ornon-linear combinations as appropriate to more accurately capture thedynamic physiological baselines of an individual or group ofindividuals.

In some aspects, the respective devices of the system 100 may supporttechniques for fertility prediction based on data collected by awearable device 104. In particular, the system 100 illustrated in FIG. 1may support techniques for predicting the fertility of a user 102, andcausing a user device 106 corresponding to the user 102 to display theindication of the predicted fertility. For example, as shown in FIG. 1,User 1 (user 102-a) may be associated with a wearable device 104-a(e.g., ring 104-a) and a user device 106-a. In this example, the ring104-a may collect data associated with the user 102-a, includingtemperature, heart rate, respiratory rate, HRV, sleep data, and thelike. In some aspects, data collected by the ring 104-a may be used todetermine a user's fertility prediction during which User 1 isconsidered fertile or infertile. Predicting the user's fertility may beperformed by any of the components of the system 100, including the ring104-a, the user device 106-a associated with User 1, the one or moreservers 110, or any combination thereof. Upon determining the user'sfertility prediction, the system 100 may selectively cause the GUI ofthe user device 106-a to display the indication of the determined userfertility prediction.

In some implementations, upon receiving physiological data (e.g.,including temperature data), the system 100 may determine a time seriesof temperature values taken over a plurality of days. The time seriesmay include a plurality of menstrual cycles. The system 100 maydetermine a plurality of menstrual cycle length parameters associatedwith the plurality of menstrual cycles. The menstrual cycle lengthparameters may include at least one of an average menstrual cyclelength, a standard deviation menstrual cycle length, an averagefollicular phase length, an average luteal phase length, a range ofmenstrual cycle lengths, a quantity of anovulatory cycles, or acombination thereof. In such cases, the system 100 may determine theuser fertility prediction based on determining the plurality ofmenstrual cycle length parameters. The user fertility prediction may bean example determining that the user is fertile, determining that theuser in infertile, and/or predicting that the user may be fertile orinfertile in the future.

In some cases, the system 100 may selectively adjust Readiness Scoresfor the user 102-a based on the determined fertility predictions. Insome implementations, the system 100 may generate alerts, messages, orrecommendations for User 1 (e.g., via the ring 104-a, user device 106-a,or both) based on the determined user fertility prediction, where thealerts may provide insights regarding the determined user fertilityprediction, such as a timing and/or duration of the determined userfertility prediction. In some cases, the messages may provide insightregarding recommendations associated with determined user fertilityprediction, one or more medical conditions associated with thedetermined user fertility prediction, educational videos and/or text(e.g., content) associated with the determined user fertilityprediction, or a combination thereof.

It should be appreciated by a person skilled in the art that one or moreaspects of the disclosure may be implemented in a system 100 toadditionally or alternatively solve other problems than those describedabove. Furthermore, aspects of the disclosure may provide technicalimprovements to “conventional” systems or processes as described herein.However, the description and appended drawings only include exampletechnical improvements resulting from implementing aspects of thedisclosure, and accordingly do not represent all of the technicalimprovements provided within the scope of the claims.

FIG. 2 illustrates an example of a system 200 that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure. The system 200 may implement, or beimplemented by, system 100. In particular, system 200 illustrates anexample of a ring 104 (e.g., wearable device 104), a user device 106,and a server 110, as described with reference to FIG. 1.

In some aspects, the ring 104 may be configured to be worn around auser's finger, and may determine one or more user physiologicalparameters when worn around the user's finger. Example measurements anddeterminations may include, but are not limited to, user skintemperature, pulse waveforms, respiratory rate, heart rate, HRV, bloodoxygen levels, and the like.

System 200 further includes a user device 106 (e.g., a smartphone) incommunication with the ring 104. For example, the ring 104 may be inwireless and/or wired communication with the user device 106. In someimplementations, the ring 104 may send measured and processed data(e.g., temperature data, photoplethysmogram (PPG) data,motion/accelerometer data, ring input data, and the like) to the userdevice 106. The user device 106 may also send data to the ring 104, suchas ring 104 firmware/configuration updates. The user device 106 mayprocess data. In some implementations, the user device 106 may transmitdata to the server 110 for processing and/or storage.

The ring 104 may include a housing 205 that may include an inner housing205-a and an outer housing 205-b. In some aspects, the housing 205 ofthe ring 104 may store or otherwise include various components of thering including, but not limited to, device electronics, a power source(e.g., battery 210, and/or capacitor), one or more substrates (e.g.,printable circuit boards) that interconnect the device electronicsand/or power source, and the like. The device electronics may includedevice modules (e.g., hardware/software), such as: a processing module230-a, a memory 215, a communication module 220-a, a power module 225,and the like. The device electronics may also include one or moresensors. Example sensors may include one or more temperature sensors240, a PPG sensor assembly (e.g., PPG system 235), and one or moremotion sensors 245.

The sensors may include associated modules (not illustrated) configuredto communicate with the respective components/modules of the ring 104,and generate signals associated with the respective sensors. In someaspects, each of the components/modules of the ring 104 may becommunicatively coupled to one another via wired or wirelessconnections. Moreover, the ring 104 may include additional and/oralternative sensors or other components that are configured to collectphysiological data from the user, including light sensors (e.g., LEDs),oximeters, and the like.

The ring 104 shown and described with reference to FIG. 2 is providedsolely for illustrative purposes. As such, the ring 104 may includeadditional or alternative components as those illustrated in FIG. 2.Other rings 104 that provide functionality described herein may befabricated. For example, rings 104 with fewer components (e.g., sensors)may be fabricated. In a specific example, a ring 104 with a singletemperature sensor 240 (or other sensor), a power source, and deviceelectronics configured to read the single temperature sensor 240 (orother sensor) may be fabricated. In another specific example, atemperature sensor 240 (or other sensor) may be attached to a user'sfinger (e.g., using a clamps, spring loaded clamps, etc.). In this case,the sensor may be wired to another computing device, such as a wristworn computing device that reads the temperature sensor 240 (or othersensor). In other examples, a ring 104 that includes additional sensorsand processing functionality may be fabricated.

The housing 205 may include one or more housing 205 components. Thehousing 205 may include an outer housing 205-b component (e.g., a shell)and an inner housing 205-a component (e.g., a molding). The housing 205may include additional components (e.g., additional layers) notexplicitly illustrated in FIG. 2. For example, in some implementations,the ring 104 may include one or more insulating layers that electricallyinsulate the device electronics and other conductive materials (e.g.,electrical traces) from the outer housing 205-b (e.g., a metal outerhousing 205-b). The housing 205 may provide structural support for thedevice electronics, battery 210, substrate(s), and other components. Forexample, the housing 205 may protect the device electronics, battery210, and substrate(s) from mechanical forces, such as pressure andimpacts. The housing 205 may also protect the device electronics,battery 210, and substrate(s) from water and/or other chemicals.

The outer housing 205-b may be fabricated from one or more materials. Insome implementations, the outer housing 205-b may include a metal, suchas titanium, that may provide strength and abrasion resistance at arelatively light weight. The outer housing 205-b may also be fabricatedfrom other materials, such polymers. In some implementations, the outerhousing 205-b may be protective as well as decorative.

The inner housing 205-a may be configured to interface with the user'sfinger. The inner housing 205-a may be formed from a polymer (e.g., amedical grade polymer) or other material. In some implementations, theinner housing 205-a may be transparent. For example, the inner housing205-a may be transparent to light emitted by the PPG light emittingdiodes (LEDs). In some implementations, the inner housing 205-acomponent may be molded onto the outer housing 205-b. For example, theinner housing 205-a may include a polymer that is molded (e.g.,injection molded) to fit into an outer housing 205-b metallic shell.

The ring 104 may include one or more substrates (not illustrated). Thedevice electronics and battery 210 may be included on the one or moresubstrates. For example, the device electronics and battery 210 may bemounted on one or more substrates. Example substrates may include one ormore printed circuit boards (PCBs), such as flexible PCB (e.g.,polyimide). In some implementations, the electronics/battery 210 mayinclude surface mounted devices (e.g., surface-mount technology (SMT)devices) on a flexible PCB. In some implementations, the one or moresubstrates (e.g., one or more flexible PCBs) may include electricaltraces that provide electrical communication between device electronics.The electrical traces may also connect the battery 210 to the deviceelectronics.

The device electronics, battery 210, and substrates may be arranged inthe ring 104 in a variety of ways. In some implementations, onesubstrate that includes device electronics may be mounted along thebottom of the ring 104 (e.g., the bottom half), such that the sensors(e.g., PPG system 235, temperature sensors 240, motion sensors 245, andother sensors) interface with the underside of the user's finger. Inthese implementations, the battery 210 may be included along the topportion of the ring 104 (e.g., on another substrate).

The various components/modules of the ring 104 represent functionality(e.g., circuits and other components) that may be included in the ring104. Modules may include any discrete and/or integrated electroniccircuit components that implement analog and/or digital circuits capableof producing the functions attributed to the modules herein. Forexample, the modules may include analog circuits (e.g., amplificationcircuits, filtering circuits, analog/digital conversion circuits, and/orother signal conditioning circuits). The modules may also includedigital circuits (e.g., combinational or sequential logic circuits,memory circuits etc.).

The memory 215 (memory module) of the ring 104 may include any volatile,non-volatile, magnetic, or electrical media, such as a random accessmemory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM),electrically-erasable programmable ROM (EEPROM), flash memory, or anyother memory device. The memory 215 may store any of the data describedherein. For example, the memory 215 may be configured to store data(e.g., motion data, temperature data, PPG data) collected by therespective sensors and PPG system 235. Furthermore, memory 215 mayinclude instructions that, when executed by one or more processingcircuits, cause the modules to perform various functions attributed tothe modules herein. The device electronics of the ring 104 describedherein are only example device electronics. As such, the types ofelectronic components used to implement the device electronics may varybased on design considerations.

The functions attributed to the modules of the ring 104 described hereinmay be embodied as one or more processors, hardware, firmware, software,or any combination thereof. Depiction of different features as modulesis intended to highlight different functional aspects and does notnecessarily imply that such modules must be realized by separatehardware/software components. Rather, functionality associated with oneor more modules may be performed by separate hardware/softwarecomponents or integrated within common hardware/software components.

The processing module 230-a of the ring 104 may include one or moreprocessors (e.g., processing units), microcontrollers, digital signalprocessors, systems on a chip (SOCs), and/or other processing devices.The processing module 230-a communicates with the modules included inthe ring 104. For example, the processing module 230-a maytransmit/receive data to/from the modules and other components of thering 104, such as the sensors. As described herein, the modules may beimplemented by various circuit components. Accordingly, the modules mayalso be referred to as circuits (e.g., a communication circuit and powercircuit).

The processing module 230-a may communicate with the memory 215. Thememory 215 may include computer-readable instructions that, whenexecuted by the processing module 230-a, cause the processing module230-a to perform the various functions attributed to the processingmodule 230-a herein. In some implementations, the processing module230-a (e.g., a microcontroller) may include additional featuresassociated with other modules, such as communication functionalityprovided by the communication module 220-a (e.g., an integratedBluetooth Low Energy transceiver) and/or additional onboard memory 215.

The communication module 220-a may include circuits that providewireless and/or wired communication with the user device 106 (e.g.,communication module 220-b of the user device 106). In someimplementations, the communication modules 220-a, 220-b may includewireless communication circuits, such as Bluetooth circuits and/or Wi-Ficircuits. In some implementations, the communication modules 220-a,220-b can include wired communication circuits, such as Universal SerialBus (USB) communication circuits. Using the communication module 220-a,the ring 104 and the user device 106 may be configured to communicatewith each other. The processing module 230-a of the ring may beconfigured to transmit/receive data to/from the user device 106 via thecommunication module 220-a. Example data may include, but is not limitedto, motion data, temperature data, pulse waveforms, heart rate data, HRVdata, PPG data, and status updates (e.g., charging status, batterycharge level, and/or ring 104 configuration settings). The processingmodule 230-a of the ring may also be configured to receive updates(e.g., software/firmware updates) and data from the user device 106.

The ring 104 may include a battery 210 (e.g., a rechargeable battery210). An example battery 210 may include a Lithium-Ion orLithium-Polymer type battery 210, although a variety of battery 210options are possible. The battery 210 may be wirelessly charged. In someimplementations, the ring 104 may include a power source other than thebattery 210, such as a capacitor. The power source (e.g., battery 210 orcapacitor) may have a curved geometry that matches the curve of the ring104. In some aspects, a charger or other power source may includeadditional sensors that may be used to collect data in addition to, orwhich supplements, data collected by the ring 104 itself. Moreover, acharger or other power source for the ring 104 may function as a userdevice 106, in which case the charger or other power source for the ring104 may be configured to receive data from the ring 104, store and/orprocess data received from the ring 104, and communicate data betweenthe ring 104 and the servers 110.

In some aspects, the ring 104 includes a power module 225 that maycontrol charging of the battery 210. For example, the power module 225may interface with an external wireless charger that charges the battery210 when interfaced with the ring 104. The charger may include a datumstructure that mates with a ring 104 datum structure to create aspecified orientation with the ring 104 during 104 charging. The powermodule 225 may also regulate voltage(s) of the device electronics,regulate power output to the device electronics, and monitor the stateof charge of the battery 210. In some implementations, the battery 210may include a protection circuit module (PCM) that protects the battery210 from high current discharge, over voltage during 104 charging, andunder voltage during 104 discharge. The power module 225 may alsoinclude electro-static discharge (ESD) protection.

The one or more temperature sensors 240 may be electrically coupled tothe processing module 230-a. The temperature sensor 240 may beconfigured to generate a temperature signal (e.g., temperature data)that indicates a temperature read or sensed by the temperature sensor240. The processing module 230-a may determine a temperature of the userin the location of the temperature sensor 240. For example, in the ring104, temperature data generated by the temperature sensor 240 mayindicate a temperature of a user at the user's finger (e.g., skintemperature). In some implementations, the temperature sensor 240 maycontact the user's skin. In other implementations, a portion of thehousing 205 (e.g., the inner housing 205-a) may form a barrier (e.g., athin, thermally conductive barrier) between the temperature sensor 240and the user's skin. In some implementations, portions of the ring 104configured to contact the user's finger may have thermally conductiveportions and thermally insulative portions. The thermally conductiveportions may conduct heat from the user's finger to the temperaturesensors 240. The thermally insulative portions may insulate portions ofthe ring 104 (e.g., the temperature sensor 240) from ambienttemperature.

In some implementations, the temperature sensor 240 may generate adigital signal (e.g., temperature data) that the processing module 230-amay use to determine the temperature. As another example, in cases wherethe temperature sensor 240 includes a passive sensor, the processingmodule 230-a (or a temperature sensor 240 module) may measure acurrent/voltage generated by the temperature sensor 240 and determinethe temperature based on the measured current/voltage. Exampletemperature sensors 240 may include a thermistor, such as a negativetemperature coefficient (NTC) thermistor, or other types of sensorsincluding resistors, transistors, diodes, and/or otherelectrical/electronic components.

The processing module 230-a may sample the user's temperature over time.For example, the processing module 230-a may sample the user'stemperature according to a sampling rate. An example sampling rate mayinclude one sample per second, although the processing module 230-a maybe configured to sample the temperature signal at other sampling ratesthat are higher or lower than one sample per second. In someimplementations, the processing module 230-a may sample the user'stemperature continuously throughout the day and night. Sampling at asufficient rate (e.g., one sample per second, one sample per minute, andthe like) throughout the day may provide sufficient temperature data foranalysis described herein.

The processing module 230-a may store the sampled temperature data inmemory 215. In some implementations, the processing module 230-a mayprocess the sampled temperature data. For example, the processing module230-a may determine average temperature values over a period of time. Inone example, the processing module 230-a may determine an averagetemperature value each minute by summing all temperature valuescollected over the minute and dividing by the number of samples over theminute. In a specific example where the temperature is sampled at onesample per second, the average temperature may be a sum of all sampledtemperatures for one minute divided by sixty seconds. The memory 215 maystore the average temperature values over time. In some implementations,the memory 215 may store average temperatures (e.g., one per minute)instead of sampled temperatures in order to conserve memory 215.

The sampling rate, which may be stored in memory 215, may beconfigurable. In some implementations, the sampling rate may be the samethroughout the day and night. In other implementations, the samplingrate may be changed throughout the day/night. In some implementations,the ring 104 may filter/reject temperature readings, such as largespikes in temperature that are not indicative of physiological changes(e.g., a temperature spike from a hot shower). In some implementations,the ring 104 may filter/reject temperature readings that may not bereliable due to other factors, such as excessive motion during 104exercise (e.g., as indicated by a motion sensor 245).

The ring 104 (e.g., communication module) may transmit the sampledand/or average temperature data to the user device 106 for storageand/or further processing. The user device 106 may transfer the sampledand/or average temperature data to the server 110 for storage and/orfurther processing.

Although the ring 104 is illustrated as including a single temperaturesensor 240, the ring 104 may include multiple temperature sensors 240 inone or more locations, such as arranged along the inner housing 205-anear the user's finger. In some implementations, the temperature sensors240 may be stand-alone temperature sensors 240. Additionally, oralternatively, one or more temperature sensors 240 may be included withother components (e.g., packaged with other components), such as withthe accelerometer and/or processor.

The processing module 230-a may acquire and process data from multipletemperature sensors 240 in a similar manner described with respect to asingle temperature sensor 240. For example, the processing module 230may individually sample, average, and store temperature data from eachof the multiple temperature sensors 240. In other examples, theprocessing module 230-a may sample the sensors at different rates andaverage/store different values for the different sensors. In someimplementations, the processing module 230-a may be configured todetermine a single temperature based on the average of two or moretemperatures determined by two or more temperature sensors 240 indifferent locations on the finger.

The temperature sensors 240 on the ring 104 may acquire distaltemperatures at the user's finger (e.g., any finger). For example, oneor more temperature sensors 240 on the ring 104 may acquire a user'stemperature from the underside of a finger or at a different location onthe finger. In some implementations, the ring 104 may continuouslyacquire distal temperature (e.g., at a sampling rate). Although distaltemperature measured by a ring 104 at the finger is described herein,other devices may measure temperature at the same/different locations.In some cases, the distal temperature measured at a user's finger maydiffer from the temperature measured at a user's wrist or other externalbody location. Additionally, the distal temperature measured at a user'sfinger (e.g., a “shell” temperature) may differ from the user's coretemperature. As such, the ring 104 may provide a useful temperaturesignal that may not be acquired at other internal/external locations ofthe body. In some cases, continuous temperature measurement at thefinger may capture temperature fluctuations (e.g., small or largefluctuations) that may not be evident in core temperature. For example,continuous temperature measurement at the finger may captureminute-to-minute or hour-to-hour temperature fluctuations that provideadditional insight that may not be provided by other temperaturemeasurements elsewhere in the body.

The ring 104 may include a PPG system 235. The PPG system 235 mayinclude one or more optical transmitters that transmit light. The PPGsystem 235 may also include one or more optical receivers that receivelight transmitted by the one or more optical transmitters. An opticalreceiver may generate a signal (hereinafter “PPG” signal) that indicatesan amount of light received by the optical receiver. The opticaltransmitters may illuminate a region of the user's finger. The PPGsignal generated by the PPG system 235 may indicate the perfusion ofblood in the illuminated region. For example, the PPG signal mayindicate blood volume changes in the illuminated region caused by auser's pulse pressure. The processing module 230-a may sample the PPGsignal and determine a user's pulse waveform based on the PPG signal.The processing module 230-a may determine a variety of physiologicalparameters based on the user's pulse waveform, such as a user'srespiratory rate, heart rate, HRV, oxygen saturation, and othercirculatory parameters.

In some implementations, the PPG system 235 may be configured as areflective PPG system 235 in which the optical receiver(s) receivetransmitted light that is reflected through the region of the user'sfinger. In some implementations, the PPG system 235 may be configured asa transmissive PPG system 235 in which the optical transmitter(s) andoptical receiver(s) are arranged opposite to one another, such thatlight is transmitted directly through a portion of the user's finger tothe optical receiver(s).

The number and ratio of transmitters and receivers included in the PPGsystem 235 may vary. Example optical transmitters may includelight-emitting diodes (LEDs). The optical transmitters may transmitlight in the infrared spectrum and/or other spectrums. Example opticalreceivers may include, but are not limited to, photosensors,phototransistors, and photodiodes. The optical receivers may beconfigured to generate PPG signals in response to the wavelengthsreceived from the optical transmitters. The location of the transmittersand receivers may vary. Additionally, a single device may includereflective and/or transmissive PPG systems 235.

The PPG system 235 illustrated in FIG. 2 may include a reflective PPGsystem 235 in some implementations. In these implementations, the PPGsystem 235 may include a centrally located optical receiver (e.g., atthe bottom of the ring 104) and two optical transmitters located on eachside of the optical receiver. In this implementation, the PPG system 235(e.g., optical receiver) may generate the PPG signal based on lightreceived from one or both of the optical transmitters. In otherimplementations, other placements, combinations, and/or configurationsof one or more optical transmitters and/or optical receivers arecontemplated.

The processing module 230-a may control one or both of the opticaltransmitters to transmit light while sampling the PPG signal generatedby the optical receiver. In some implementations, the processing module230-a may cause the optical transmitter with the stronger receivedsignal to transmit light while sampling the PPG signal generated by theoptical receiver. For example, the selected optical transmitter maycontinuously emit light while the PPG signal is sampled at a samplingrate (e.g., 250 Hz).

Sampling the PPG signal generated by the PPG system 235 may result in apulse waveform that may be referred to as a “PPG.” The pulse waveformmay indicate blood pressure vs time for multiple cardiac cycles. Thepulse waveform may include peaks that indicate cardiac cycles.Additionally, the pulse waveform may include respiratory inducedvariations that may be used to determine respiration rate. Theprocessing module 230-a may store the pulse waveform in memory 215 insome implementations. The processing module 230-a may process the pulsewaveform as it is generated and/or from memory 215 to determine userphysiological parameters described herein.

The processing module 230-a may determine the user's heart rate based onthe pulse waveform. For example, the processing module 230-a maydetermine heart rate (e.g., in beats per minute) based on the timebetween peaks in the pulse waveform. The time between peaks may bereferred to as an interbeat interval (IBI). The processing module 230-amay store the determined heart rate values and IBI values in memory 215.

The processing module 230-a may determine HRV over time. For example,the processing module 230-a may determine HRV based on the variation inthe IBIs. The processing module 230-a may store the HRV values over timein the memory 215. Moreover, the processing module 230-a may determinethe user's respiratory rate over time. For example, the processingmodule 230-a may determine respiratory rate based on frequencymodulation, amplitude modulation, or baseline modulation of the user'sIBI values over a period of time. Respiratory rate may be calculated inbreaths per minute or as another breathing rate (e.g., breaths per 30seconds). The processing module 230-a may store user respiratory ratevalues over time in the memory 215.

The ring 104 may include one or more motion sensors 245, such as one ormore accelerometers (e.g., 6-D accelerometers) and/or one or moregyroscopes (gyros). The motion sensors 245 may generate motion signalsthat indicate motion of the sensors. For example, the ring 104 mayinclude one or more accelerometers that generate acceleration signalsthat indicate acceleration of the accelerometers. As another example,the ring 104 may include one or more gyro sensors that generate gyrosignals that indicate angular motion (e.g., angular velocity) and/orchanges in orientation. The motion sensors 245 may be included in one ormore sensor packages. An example accelerometer/gyro sensor is a BoschBMl160 inertial micro electro-mechanical system (MEMS) sensor that maymeasure angular rates and accelerations in three perpendicular axes.

The processing module 230-a may sample the motion signals at a samplingrate (e.g., 50 Hz) and determine the motion of the ring 104 based on thesampled motion signals. For example, the processing module 230-a maysample acceleration signals to determine acceleration of the ring 104.As another example, the processing module 230-a may sample a gyro signalto determine angular motion. In some implementations, the processingmodule 230-a may store motion data in memory 215. Motion data mayinclude sampled motion data as well as motion data that is calculatedbased on the sampled motion signals (e.g., acceleration and angularvalues).

The ring 104 may store a variety of data described herein. For example,the ring 104 may store temperature data, such as raw sampled temperaturedata and calculated temperature data (e.g., average temperatures). Asanother example, the ring 104 may store PPG signal data, such as pulsewaveforms and data calculated based on the pulse waveforms (e.g., heartrate values, IBI values, HRV values, and respiratory rate values). Thering 104 may also store motion data, such as sampled motion data thatindicates linear and angular motion.

The ring 104, or other computing device, may calculate and storeadditional values based on the sampled/calculated physiological data.For example, the processing module 230 may calculate and store variousmetrics, such as sleep metrics (e.g., a Sleep Score), activity metrics,and readiness metrics. In some implementations, additionalvalues/metrics may be referred to as “derived values.” The ring 104, orother computing/wearable device, may calculate a variety ofvalues/metrics with respect to motion. Example derived values for motiondata may include, but are not limited to, motion count values,regularity values, intensity values, metabolic equivalence of taskvalues (METs), and orientation values. Motion counts, regularity values,intensity values, and METs may indicate an amount of user motion (e.g.,velocity/acceleration) over time. Orientation values may indicate howthe ring 104 is oriented on the user's finger and if the ring 104 isworn on the left hand or right hand.

In some implementations, motion counts and regularity values may bedetermined by counting a number of acceleration peaks within one or moreperiods of time (e.g., one or more 30 second to 1 minute periods).Intensity values may indicate a number of movements and the associatedintensity (e.g., acceleration values) of the movements. The intensityvalues may be categorized as low, medium, and high, depending onassociated threshold acceleration values. METs may be determined basedon the intensity of movements during a period of time (e.g., 30seconds), the regularity/irregularity of the movements, and the numberof movements associated with the different intensities.

In some implementations, the processing module 230-a may compress thedata stored in memory 215. For example, the processing module 230-a maydelete sampled data after making calculations based on the sampled data.As another example, the processing module 230-a may average data overlonger periods of time in order to reduce the number of stored values.In a specific example, if average temperatures for a user over oneminute are stored in memory 215, the processing module 230-a maycalculate average temperatures over a five minute time period forstorage, and then subsequently erase the one minute average temperaturedata. The processing module 230-a may compress data based on a varietyof factors, such as the total amount of used/available memory 215 and/oran elapsed time since the ring 104 last transmitted the data to the userdevice 106.

Although a user's physiological parameters may be measured by sensorsincluded on a ring 104, other devices may measure a user's physiologicalparameters. For example, although a user's temperature may be measuredby a temperature sensor 240 included in a ring 104, other devices maymeasure a user's temperature. In some examples, other wearable devices(e.g., wrist devices) may include sensors that measure userphysiological parameters. Additionally, medical devices, such asexternal medical devices (e.g., wearable medical devices) and/orimplantable medical devices, may measure a user's physiologicalparameters. One or more sensors on any type of computing device may beused to implement the techniques described herein.

The physiological measurements may be taken continuously throughout theday and/or night. In some implementations, the physiologicalmeasurements may be taken during 104 portions of the day and/or portionsof the night. In some implementations, the physiological measurementsmay be taken in response to determining that the user is in a specificstate, such as an active state, resting state, and/or a sleeping state.For example, the ring 104 can make physiological measurements in aresting/sleep state in order to acquire cleaner physiological signals.In one example, the ring 104 or other device/system may detect when auser is resting and/or sleeping and acquire physiological parameters(e.g., temperature) for that detected state. The devices/systems may usethe resting/sleep physiological data and/or other data when the user isin other states in order to implement the techniques of the presentdisclosure.

In some implementations, as described previously herein, the ring 104may be configured to collect, store, and/or process data, and maytransfer any of the data described herein to the user device 106 forstorage and/or processing. In some aspects, the user device 106 includesa wearable application 250, an operating system (OS), a web browserapplication (e.g., web browser 280), one or more additionalapplications, and a GUI 275. The user device 106 may further includeother modules and components, including sensors, audio devices, hapticfeedback devices, and the like. The wearable application 250 may includean example of an application (e.g., “app”) that may be installed on theuser device 106. The wearable application 250 may be configured toacquire data from the ring 104, store the acquired data, and process theacquired data as described herein. For example, the wearable application250 may include a user interface (UI) module 255, an acquisition module260, a processing module 230-b, a communication module 220-b, and astorage module (e.g., database 265) configured to store applicationdata.

The various data processing operations described herein may be performedby the ring 104, the user device 106, the servers 110, or anycombination thereof. For example, in some cases, data collected by thering 104 may be pre-processed and transmitted to the user device 106. Inthis example, the user device 106 may perform some data processingoperations on the received data, may transmit the data to the servers110 for data processing, or both. For instance, in some cases, the userdevice 106 may perform processing operations that require relatively lowprocessing power and/or operations that require a relatively lowlatency, whereas the user device 106 may transmit the data to theservers 110 for processing operations that require relatively highprocessing power and/or operations that may allow relatively higherlatency.

In some aspects, the ring 104, user device 106, and server 110 of thesystem 200 may be configured to evaluate sleep patterns for a user. Inparticular, the respective components of the system 200 may be used tocollect data from a user via the ring 104, and generate one or morescores (e.g., Sleep Score, Readiness Score) for the user based on thecollected data. For example, as noted previously herein, the ring 104 ofthe system 200 may be worn by a user to collect data from the user,including temperature, heart rate, HRV, and the like. Data collected bythe ring 104 may be used to determine when the user is asleep in orderto evaluate the user's sleep for a given “sleep day.” In some aspects,scores may be calculated for the user for each respective sleep day,such that a first sleep day is associated with a first set of scores,and a second sleep day is associated with a second set of scores. Scoresmay be calculated for each respective sleep day based on data collectedby the ring 104 during the respective sleep day. Scores may include, butare not limited to, Sleep Scores, Readiness Scores, and the like.

In some cases, “sleep days” may align with the traditional calendardays, such that a given sleep day runs from midnight to midnight of therespective calendar day. In other cases, sleep days may be offsetrelative to calendar days. For example, sleep days may run from 6:00 pm(18:00) of a calendar day until 6:00 pm (18:00) of the subsequentcalendar day. In this example, 6:00 pm may serve as a “cut-off time,”where data collected from the user before 6:00 pm is counted for thecurrent sleep day, and data collected from the user after 6:00 pm iscounted for the subsequent sleep day. Due to the fact that mostindividuals sleep the most at night, offsetting sleep days relative tocalendar days may enable the system 200 to evaluate sleep patterns forusers in such a manner that is consistent with their sleep schedules. Insome cases, users may be able to selectively adjust (e.g., via the GUI)a timing of sleep days relative to calendar days so that the sleep daysare aligned with the duration of time in which the respective userstypically sleep.

In some implementations, each overall score for a user for eachrespective day (e.g., Sleep Score, Readiness Score) may bedetermined/calculated based on one or more “contributors,” “factors,” or“contributing factors.” For example, a user's overall Sleep Score may becalculated based on a set of contributors, including: total sleep,efficiency, restfulness, REM sleep, deep sleep, latency, timing, or anycombination thereof. The Sleep Score may include any quantity ofcontributors. The “total sleep” contributor may refer to the sum of allsleep periods of the sleep day. The “efficiency” contributor may reflectthe percentage of time spent asleep compared to time spent awake whilein bed, and may be calculated using the efficiency average of long sleepperiods (e.g., primary sleep period) of the sleep day, weighted by aduration of each sleep period. The “restfulness” contributor mayindicate how restful the user's sleep is, and may be calculated usingthe average of all sleep periods of the sleep day, weighted by aduration of each period. The restfulness contributor may be based on a“wake up count” (e.g., sum of all the wake-ups (when user wakes up)detected during different sleep periods), excessive movement, and a “gotup count” (e.g., sum of all the got-ups (when user gets out of bed)detected during the different sleep periods).

The “REM sleep” contributor may refer to a sum total of REM sleepdurations across all sleep periods of the sleep day including REM sleep.Similarly, the “deep sleep” contributor may refer to a sum total of deepsleep durations across all sleep periods of the sleep day including deepsleep. The “latency” contributor may signify how long (e.g., average,median, longest) the user takes to go to sleep, and may be calculatedusing the average of long sleep periods throughout the sleep day,weighted by a duration of each period and the number of such periods(e.g., consolidation of a given sleep stage or sleep stages may be itsown contributor or weight other contributors). Lastly, the “timing”contributor may refer to a relative timing of sleep periods within thesleep day and/or calendar day, and may be calculated using the averageof all sleep periods of the sleep day, weighted by a duration of eachperiod.

By way of another example, a user's overall Readiness Score may becalculated based on a set of contributors, including: sleep, sleepbalance, heart rate, HRV balance, recovery index, temperature, activity,activity balance, or any combination thereof. The Readiness Score mayinclude any quantity of contributors. The “sleep” contributor may referto the combined Sleep Score of all sleep periods within the sleep day.The “sleep balance” contributor may refer to a cumulative duration ofall sleep periods within the sleep day. In particular, sleep balance mayindicate to a user whether the sleep that the user has been getting oversome duration of time (e.g., the past two weeks) is in balance with theuser's needs. Typically, adults need 7-9 hours of sleep a night to stayhealthy, alert, and to perform at their best both mentally andphysically. However, it is normal to have an occasional night of badsleep, so the sleep balance contributor takes into account long-termsleep patterns to determine whether each user's sleep needs are beingmet. The “resting heart rate” contributor may indicate a lowest heartrate from the longest sleep period of the sleep day (e.g., primary sleepperiod) and/or the lowest heart rate from naps occurring after theprimary sleep period.

Continuing with reference to the “contributors” (e.g., factors,contributing factors) of the Readiness Score, the “HRV balance”contributor may indicate a highest HRV average from the primary sleepperiod and the naps happening after the primary sleep period. The HRVbalance contributor may help users keep track of their recovery statusby comparing their HRV trend over a first time period (e.g., two weeks)to an average HRV over some second, longer time period (e.g., threemonths). The “recovery index” contributor may be calculated based on thelongest sleep period. Recovery index measures how long it takes for auser's resting heart rate to stabilize during the night. A sign of avery good recovery is that the user's resting heart rate stabilizesduring the first half of the night, at least six hours before the userwakes up, leaving the body time to recover for the next day. The “bodytemperature” contributor may be calculated based on the longest sleepperiod (e.g., primary sleep period) or based on a nap happening afterthe longest sleep period if the user's highest temperature during thenap is at least 0.5° C. higher than the highest temperature during thelongest period. In some aspects, the ring may measure a user's bodytemperature while the user is asleep, and the system 200 may display theuser's average temperature relative to the user's baseline temperature.If a user's body temperature is outside of their normal range (e.g.,clearly above or below 0.0), the body temperature contributor may behighlighted (e.g., go to a “Pay attention” state) or otherwise generatean alert for the user.

In some aspects, the system 200 may support techniques for fertilityprediction. In particular, the respective components of the system 200may be used to determine a plurality of menstrual cycle lengthparameters in a time series representing the user's temperature overtime. A fertility score of the user may be predicted by leveragingtemperature sensors on the ring 104 of the system 200. In some cases,the fertility prediction of the user may be determined by determiningthe plurality of menstrual cycle length parameters in the time seriesrepresenting the user's temperature over time and determining the userfertility prediction based on the determined menstrual cycle lengthparameters. The menstrual cycle length parameters may be an example ofat least one of an average menstrual cycle length, a standard deviationmenstrual cycle length, an average follicular phase length, an averageluteal phase length, a range of menstrual cycle lengths, a quantity ofanovulatory cycles, or a combination thereof.

For example, as noted previously herein, the ring 104 of the system 200may be worn by a user to collect data from the user, includingtemperature, heart rate, respiratory data, HRV data, sleep data, and thelike. The ring 104 of the system 200 may collect the physiological datafrom the user based on temperature sensors and measurements extractedfrom arterial blood flow (e.g., using PPG signals). The physiologicaldata may be collected continuously. In some implementations, theprocessing module 230-a may sample the user's temperature continuouslythroughout the day and night. Sampling at a sufficient rate (e.g., onesample per minute) throughout the day and/or night may providesufficient temperature data for analysis described herein. In someimplementations, the ring 104 may continuously acquire temperature data(e.g., at a sampling rate). In some examples, even though temperature iscollected continuously, the system 200 may leverage other informationabout the user that it has collected or otherwise derived (e.g., sleepstage, activity levels, illness onset, etc.) to select a representativetemperature for a particular day that is an accurate representation ofthe underlying physiological phenomenon.

In contrast, systems that require a user to manually take theirtemperature each day and/or systems that measure temperaturecontinuously but lack any other contextual information about the usermay select inaccurate or inconsistent temperature values for theirfertility prediction, leading to inaccurate predictions and decreaseduser experience. In contrast, data collected by the ring 104 may be usedto accurately determine the user's fertility prediction. Predictionfertility scores and related techniques are further shown and describedwith reference to FIG. 3.

FIG. 3 illustrates an example of a system 300 that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure. The system 300 may implement, or beimplemented by, system 100, system 200, or both. In particular, system300 illustrates an example of a ring 104 (e.g., wearable device 104), auser device 106, and a server 110, as described with reference to FIG.1.

The ring 305 may acquire temperature data 320, heart rate data 325,respiratory rate data 330, HRV data 335, and sleep data 340. In suchcases, the ring 305 may transmit temperature data 320, heart rate data325, respiratory rate data 330, HRV data 335, and sleep data 340 to theuser device 310. The temperature data 320 may include continuous daytimetemperature data. In some cases, multiple devices may acquirephysiological data. For example, a first computing device (e.g., userdevice 310) and a second computing device (e.g., the ring 305) mayacquire temperature data 320, heart rate data 325, respiratory rate data330, HRV data 335, sleep data 340, or a combination thereof.

For example, the ring 305 may acquire user physiological data, such asuser temperature data 320, respiratory rate data 330, heart rate data325, HRV data 335, and sleep data 340, galvanic skin response, bloodoxygen saturation, actigraphy, location data, and/or other userphysiological data. For example, the ring 305 may acquire raw data andconvert the raw data to features with daily granularity. In someimplementations, different granularity input data may be used. The ring305 may send the data to another computing device, such as a mobiledevice (e.g., user device 310) for further processing. For example, thedata may be input into the ring application 345 and stored in theapplication data 355, which may automatically trigger a synchronizationto a cloud data lake (e.g., on the server 315).

In some cases, a pipeline (e.g., a FeatureMaker pipeline) in the cloud(e.g., server 315) may be triggered by the arrival of new data availableon eligible users in the cloud data lake. The pipeline may performfurther cleaning, processing, and statistical or frequency analysis onthe data to derive a final feature set. In some implementations, thepipeline may incorporate clinically relevant information from healthcareprovider-based assessments, user self-reported clinical symptoms, orboth.

The system 300 may input user data (e.g., a prior few months) andfeatures into a preprocessing pipeline. The pipeline may smooth the data(e.g., using a 7-day smoothing window or other window). In someexamples, missing values may be imputed, such as by using a forecasterImpute method from the python package sktime or by forward filling ofthe prior median/mean or by interpolation methods. In some cases, thesystem may derive a series of cycle characteristic features (e.g., froma multivariate feature matrix). These characteristics may includefeatures, such as average cycle length, standard deviation cycle length,length of the user's average follicular phase, length of the user'saverage luteal phase, range of cycle lengths, and number of anovulatorycycles. Additional or alternative cycle characteristics may be included.

For example, the user device 310 may determine menstrual cycle lengthparameters associated with the menstrual cycles of the user based on thereceived data. In some cases, the system 300 may determine menstrualcycle length parameters associated with the menstrual cycles of the userbased on temperature data 320, respiratory rate data 330, heart ratedata 325, HRV data 335, sleep data 340, galvanic skin response, bloodoxygen saturation, activity, sleep architecture, or a combinationthereof. In some cases, the system 300 may determine a user fertilityprediction based on the determined menstrual cycle length parameters.Although the system may be implemented by a ring 305 and a user device310, any combination of computing devices described herein may implementthe features attributed to the system 300.

The user device 310-a may include a ring application 345. The ringapplication 345 may include at least modules 350 and application data355. In some cases, the application data 355 may include historicaltemperature patterns for the user and other data. The other data mayinclude temperature data 320, heart rate data 325, respiratory rate data330, HRV data 335, sleep data 340, or a combination thereof.

The ring application 345 may present a determined user fertilityprediction to the user. The ring application 345 may include anapplication data processing module that may perform data processing. Forexample, the application data processing module may include modules 350that provide functions attributed to the system 300. Example modules 350may include a daily temperature determination module, a time seriesprocessing module, a menstrual cycle length parameters module, andfertility prediction module.

The daily temperature determination module may determine dailytemperature values (e.g., by selecting a representative temperaturevalue for that day from a series of temperature values that werecollected continuously throughout the day/or night). The time seriesprocessing module may process time series data to identify the pluralityof temperature values. The menstrual cycle length parameters module maydetermine menstrual cycle length parameters associated with themenstrual cycles based on the processed time series data. The fertilityprediction module may determine the user fertility prediction based onthe processed time series data. In such cases, the system 300 mayreceive user physiological data (e.g., from a ring 305) and output dailyclassification of a user's fertility prediction. The ring application345 may store application data 355, such as acquired temperature data,other physiological data, fertility tracking data (e.g., event data),and menstrual cycle tracking data.

In some cases, the system 300 may generate fertility and/or menstrualcycle tracking data based on user physiological data (e.g., temperaturedata 320 and/or motion data). The fertility and/or menstrual cycletracking data may include a determined fertility prediction for theuser, which may be determined based on acquired user temperature data(e.g., daily temperature data 320) over an analysis time period (e.g., aperiod of weeks/months). For example, the system 300 may receivephysiological data associated with a user from a wearable device (e.g.,ring 305). The physiological data may include at least temperature data320, heart rate data 325, respiratory rate data 330, HRV data 335, sleepdata 340, or a combination thereof. For example, the system 300 acquiresuser physiological data over an analysis time period (e.g., a pluralityof days). In such cases, the system 300 may acquire and process userphysiological data over an analysis time period to generate one or moretime series of user physiological data.

In some cases, the system 300 may acquire daily user temperature data320 over an analysis time period. For example, the system 300 maycalculate a single temperature value for each day. The system 300 mayacquire a plurality of temperature values during the day and night andprocess the acquired temperature values to determine the single dailytemperature value. In some implementations, the system 300 may determinea time series of a plurality of temperature values taken over aplurality of days based on the received temperature data 320. The system300 may determine menstrual cycle length parameters associated with themenstrual cycles based on the received temperature data 320, as furthershown and described with reference to FIG. 5. In such cases, the system300 may determine the user fertility prediction based on the receivedtemperature data 320.

In some implementations, the system 300 may determine that the receivedheart rate data 325 satisfies a threshold heart rate for the user for atleast a portion of the plurality of days. In such cases, the system 300may determine the user fertility prediction based on determining thatthe received heart rate data 325 satisfies the threshold heart rate forthe user. In some examples, the system 300 may determine that thereceived respiratory rate data 330 satisfies a threshold respiratoryrate for the user for at least a portion of the plurality of days. Insuch cases, the system 300 may determine the user fertility predictionbased on determining that the received respiratory rate data 330satisfies the threshold baseline respiratory rate for the user.

In some implementations, the system 300 may determine that the receivedHRV data 335 satisfies the threshold HRV for the user for at least aportion of the plurality of days. In such cases, the system 300 maydetermine the user fertility prediction based on determining that thereceived HRV data 335 satisfies the threshold HRV for the user. In someimplementations, the system 300 may determine that a quantity ofdetected sleep disturbances from the received sleep data 340 satisfies abaseline sleep disturbance threshold for the user for at least a portionof the plurality of days. In such cases, the system 300 may determinethe user fertility prediction based on determining that the quantity ofdetected sleep disturbances from the received sleep data 340 satisfiesthe baseline sleep disturbance threshold for the user. In such cases,the user's thresholds (e.g., temperature, heart rate, respiratory rate,heart rate variability, sleep data, and the like) may betailored-specific to the user based on historical data 360 acquired bythe system 300.

In some implementations, the system may identify personalized lifestylefactors to improve fertility treatment success. For example, the systemmay track behavioral metrics related to sleep, activity, and nutritionto help users improve fertility and guide fertility treatment. In somecases, there may be a relationship between sleep disturbance andreproductive health. Sleep disturbance may be detected in users such asusers under stress and/or shift workers that experience sleep disruptionand circadian misalignment impacting reproductive health outcomesincluding: menstrual irregularities, dysmenorrhea, reduced rates ofconception, increased miscarriages, and lower birth weights. Bymonitoring sleep data for the user and aggregate users undergoingfertility treatments, the system 300 may provide valuable insights intothe relationship between various dimensions and metrics of sleep andfertility in order to increase successful pregnancy rates.

The system 300 may detect variations in bedtime, variations in waketime,the duration and fragmentation of sleep, and changes in sleep stages(deep, light, REM) over a period of time (e.g., weeks to months). Thesemetrics may be used to identify individuals with circadian disruptions.For example, in some implementations, the system 300 may compute thedeviation in minutes between bedtime on one night compared to theprevious night for a given user. The same computation may be performedfor waketime and other sleep metrics. The mean or median ofnight-to-night changes over weeks or months may be computed for eachuser as metrics quantifying the overall amount of circadian disruptiondue to a user's behavior. These metrics may be used to identify userswith high circadian disruption, show users trends in their behaviorpatterns to help increase user awareness of behavior, and tailorinsights to users with circadian disruptions with specificrecommendations placed in a context of what's known about how a behaviorlike sleep impacts fertility.

In some implementations, the system 300 may ask a user to indicatewhether they are shift workers (e.g., during a sign-up process) orexperience other sleep disturbances. In such cases, metrics (e.g.,self-reported and signal-derived metrics of circadian disruption) may beincorporated as features in a machine learning model to predict risk forsubfertility, infertility, fertility treatment success, fertilitycomplications, the likelihood of miscarriage, or a combination thereof.The system 300 that considers the user's sleep history and quantifiesthe magnitude of circadian disruption may surface tags that may provideinput on overall health as well as fertility status (e.g., late nightmeal, back pain, chest pain). In some implementations, these factors maybe used to drive the selection and personalization of insights displayedto the users. The system 300 may provide feedback on fertility oddswhile considering menstrual cycle irregularities. For example, the usermay be provided with information related to circadian rhythms and sleephygiene. The user may be presented the option to connect with ahuman-in-the-loop live sleep coach to set goals and an action plan thatmay lead to application supported behavioral changes under the user'spersonal constraints and limitations (e.g., time, nutrition). In someimplementations, the system 300 may allow users to tag melatonin and seerelated changes in sleep and fertility, as melatonin levels andsupplementation may influence fertility outcomes (e.g., via biologicalmechanisms such as reduction of oxidative stress-mediated effects onreproductive tissues).

The system 300 may cause a GUI of the user devices 310-a, 310-b todisplay the determined user fertility prediction. In some cases, thesystem 300 may cause the GUI to display the time series. The system 300may generate a tracking GUI that includes physiological data (e.g., atleast temperature data 320), tagged events, and/or other GUI elementsdescribed herein with reference to FIG. 6. In such cases, the system 300may render ovulations, periods, luteal phases, follicular phases, andthe like in a tracking GUI.

The system 300 may generate a message 370 for display on a GUI on a userdevice 310-a or 310-b that indicates the determined user fertilityprediction. For example, the system 300 (e.g., user device 310-a orserver 315) may transmit the message 370 that indicates the determineduser fertility prediction to the user device 310-b. In such cases, theuser device 310-b may be associated with a clinician, a fertilityspecialist, a care-taker, a partner, or a combination thereof. Theprediction of the user's fertility may trigger a personalized message370 to a user highlighting the pattern detected in the temperature dataand providing an educational link about fertility.

In some implementations, the ring application 345 may notify the user ofdetermined user fertility prediction and/or prompt the user to perform avariety of tasks in the activity GUI. The notifications and prompts mayinclude text, graphics, and/or other user interface elements. Thenotifications and prompts may be included in the ring application 345such as when there is a determined user fertility prediction, the ringapplication 345 may display notifications and prompts. The user device310 may display notifications and prompts in a separate window on thehome screen and/or overlaid onto other screens (e.g., at the very top ofthe home screen). In some cases, the user device 310 may display thenotifications and prompts on a mobile device, a user's watch device, orboth.

In some cases, the system 300 may apply an algorithm (e.g., a machinelearning or deep learning algorithm) to predict the likelihood offertility success within a given treatment session. The output (e.g., aprobability and the primary explanatory drivers of that probability) maybe transmitted back to the application 345. In such cases, the userdevice 310 may display, in a personalized manner, what contributors aremost predictive during the treatment cycle. The system 300 may providepersonalized insights to highlight which of the contributors aremodifiable, thereby empowering the user to consider what is actionablefrom her perspective. In some implementations, the system 300 mayprovide a readout intended for the user to share with her provider,along with links to further web-based information about the metrics, theprior scientific validation studies, and guides for interpretation.

The system 300 may train a model, such as a regression model, to predicta user's fertility score. In some implementations, different deeplearning representations (gated recurrent units (GRUs), convolutionalneural networks (CNNs), long short-term memories (LSTMs), etc.) may beused to derive embeddings that better represent the physiology data forprediction. The model (e.g., regression classifier) may be run (e.g., ona server 315) over a period of data (e.g., the prior 6 months) todetermine a fertility score for the user. In some cases, the system 300may generate Shapley values to quantify important contributors for agiven user. Contributors with a common data source may be combined bysumming the absolute value of the Shapley values for each feature thatwas derived from a given data source (e.g., temperature). Thesecontributors may be scaled to a score between 0 and 1 using a logitfunction and then the top contributions and their relative importancemay be visualized to a user, or employed to drive personalized insightsaround the most important actionable contributors.

In some implementations, the user device 310 may store historical userdata. In some cases, the historical user data may include historicaldata 360. The historical data 360 may include historical temperaturepatterns of the user, historical heart rate patterns of the user,historical respiratory rate patterns of the user, historical HRVpatterns of the user, historical sleep patterns of the user, historicalmenstrual cycle onset events (e.g., cycle length, cycle start date,etc.) of the user, or a combination thereof. The historical data 360 maybe selected from the last few months. The historical data 360 may beused (e.g., by the user device 310 or server 315) to determine athreshold for the user, determine temperature values of the user,predict the user's fertility, or a combination thereof. The historicaldata 360 may be used by the server 315. Using the historical data 360may allow the user device 310 and/or server 315 to personalize the GUIby taking into consideration user's historical data 360.

In such cases, the user device 310 may transmit historical data 360 tothe server 315. In some cases, the transmitted historical data 360 maybe the same historical data stored in the ring application 345. In otherexamples, the historical data 360 may be different than the historicaldata stored in the ring application 345. The server 315 may receive thehistorical data 360. The server 315 may store the historical data 360 inserver data 365.

In some implementations, the user device 310 and/or server 315 may alsoreceive and store other data which may be an example of userinformation. The user information may include, but is not limited to,user age, weight, height, gender, a lifestyle summary of the user, atype of fertility treatment experienced by the user, and a quantity ofmiscarriages experienced by the user. In some implementations, the userinformation may be used as features for determining the user fertilityprediction. The server data 365 may include the other data such as userinformation.

In some implementations, the system 300 may include one or more userdevices 310 for different users. For example, the system 300 may includeuser device 310-a for a primary user and user device 310-b for a seconduser 302 associated with the primary user (e.g., partner). The userdevices 310 may measure physiological parameters of the different users,provide GUIs for the different users, and receive user input from thedifferent users. In some implementations, the different user devices 310may acquire physiological information and provide output related to awoman's health, such as menstrual cycles, ovarian cycles, illness,fertility, and/or pregnancy. In some implementations, the user device310-b may acquire physiological information related to the second user302, such as male illness and fertility.

In some implementations, the system 300 may provide GUIs that inform thesecond user 302 of relevant information. For example, the first user andthe second user 302 may share their information with one another via oneor more user devices 310, such as via a server device, mobile device, orother device. In some implementations, the second user 302 may share oneor more of their accounts (e.g., usernames, login information, etc.)and/or associated data with one another (e.g., the first user). Bysharing information between users, the system 300 may assist secondusers 302 in making health decisions related to fertility. In someimplementations, the users may be prompted (e.g., in a GUI) to sharespecific information. For example, the user may use a GUI to opt intosharing her fertility information with the second user 302. In suchcases, the user and the second user 302 may receive notifications ontheir respective user devices 310. In other examples, a second user 302may make their information (e.g., illness, fertility data, etc.)available to the user via a notification or other sharing arrangement.In such cases, the second user 302 may be an example of a clinician, afertility specialist, a care-taker, a partner, or a combination thereof.

FIG. 4 illustrates examples of timing diagrams 400 that supportfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure. The timing diagrams400 may implement, or be implemented by, aspects of the system 100,system 200, system 300, or a combination thereof. For example, in someimplementations, the timing diagrams 400 may be displayed to a user viathe GUI 275 of the user device 106, as shown in FIG. 2.

As described in further detail herein, the system may be configured todetermine the user fertility prediction based on temperature values, HRVvalues, respiratory rate values, heart rate values, or a combinationthereof. In some cases, the user's body temperature pattern, HRVpattern, respiratory rate pattern, heart rate pattern, or a combinationthereof throughout the day and night may be an indicator that maycharacterize fertility. For example, skin temperature, HRV, respiratoryrate, heart rate, or a combination thereof during the day and night maydetermine the user fertility prediction.

In some cases, the system may track a user's temperature, heart rate,HRV, and respiratory rate menstrual cycle fluctuations across herreproductive lifespan. The user's temperature, heart rate, HRV, andrespiratory rate menstrual cycle fluctuations may be example of estrogenmarkers that indicate a group of hormones that play a role in the sexualand reproductive development in women. The user may be alerted whenthere is a significant decrease in fluctuations in these estrogenmarkers, which may indicate declining fertility and approachingmenopause. In such cases, estrogen levels may be associated with changesin cardiac and respiratory features. In some cases, the decrease influctuation may be identified as early as age 31 whereas menopausetypically onsets around age 50, leaving ample time for a user to planher pregnancies and even infertility treatments.

As such, the timing diagram 400-a illustrates a relationship between auser's temperature data and a time (e.g., over a menstrual cycle). Inthis regard, the solid curved line illustrated in the timing diagram400-a may be understood to refer to the “temperature values 405.” Theuser's temperature values 405 may be relative to a baseline temperature.The menstrual cycle bins may be representative of a day of the user'smenstrual cycle. In some cases, the temperature values 405 may berepresentative temperature data for users between the ages of 18 and 47.In some cases, the temperature fluctuations may not diminish with age asmenopause approaches.

In some cases, the system (e.g., ring 104, user device 106, server 110)may receive physiological data associated with a user from a wearabledevice. The physiological data may include at least temperature data.The system may determine a time series of a plurality of temperaturevalues 405 taken over a plurality of days based on the receivedtemperature data. With reference to timing diagram 400-a, the pluralityof days may be an example of 28 days. For example, the timing diagram400-a may be an example of temperature values 405 throughout a menstrualcycle.

The system may process original time series temperature data (e.g.,temperature values 405) to determine the user fertility prediction. Thetemperature values 405 may be continuously collected by the wearabledevice. The physiological measurements may be taken continuouslythroughout the day and/or night. For example, in some implementations,the ring may be configured to acquire physiological data (e.g.,temperature data, MET data, and the like) continuously in accordancewith one or more measurement periodicities throughout the entirety ofeach day/sleep day. In other words, the ring may continuously acquirephysiological data from the user without regard to “trigger conditions”for performing such measurements. In some cases, continuous temperaturemeasurement at the finger may capture temperature fluctuations (e.g.,small or large fluctuations) that may not be evident in coretemperature. For example, continuous temperature measurement at thefinger may capture minute-to-minute or hour-to-hour temperaturefluctuations that provide additional insight that may not be provided byother temperature measurements elsewhere in the body or if the user weremanually taking their temperature once per day.

In some implementations, the system may determine the user fertilityprediction by observing a user's relative body temperature for manydays. For example, the system may determine that the receivedtemperature data (e.g., temperature values 405) satisfies a thresholdtemperature for the user for at least a portion of the plurality ofdays. In such cases, the system may determine the user fertilityprediction in response to determining that the received temperature datasatisfies the threshold temperature for the user. For example, thesystem may identify that the user's temperature begins to rise after 10days of the menstrual cycle (e.g., around day 10 of the menstrual cycle)and begins to decrease after 22 days of the menstrual cycle (e.g.,around day 22 of the menstrual cycle). In some examples, the system mayidentify the temperature values 405 after determining the time series,and identify the threshold temperature for the user.

The system may identify one or more slopes of the time series of thetemperature values 405. For example, the system may identify one orslopes of the time series of the plurality of temperature values 405after determining the time series. The system may determine the userfertility prediction based on identifying the one or more slopes of thetime series. The one or more slopes may include positive slopes,negative slopes, or both.

In some cases, the system may determine, or estimate, the temperaturemaximum and/or minimum for a user after determining the time series ofthe temperature values 405 for the user collected via the ring. Thesystem may identify the one or more slopes of the time series of theplurality of temperature values 405 based on determining the maximumand/or minimum. In some cases, calculating the difference between themaximum and minimum may determine the slope. In other examples,identifying the one or more slopes of the time series of the pluralityof temperature values 405 may be in response to computing a derivativeof the original time series temperature data (e.g., temperature values405).

As described in further detail herein, the system may be configured totrack menstrual cycles, ovulation, pregnancy, fertility, and the like.In some cases, the user's body temperature pattern throughout the daymay be an indicator that may characterize fertility. For example, skintemperature during the day may determine the user fertility prediction.As such, the timing diagram 400-a illustrates a relationship between auser's temperature data and a time (e.g., over a plurality of days).

The timing diagram 400-b illustrates a relationship between a user'srespiratory rate data and a time (e.g., over a menstrual cycle). In thisregard, the solid curved line illustrated in the timing diagram 400-bmay be understood to refer to the “respiratory rate values 410.” Theuser's respiratory rate values 410 may be relative to a baselinerespiratory rate. The menstrual cycle bins may be representative of aday of the user's menstrual cycle. In some cases, the respiratory ratevalues 410 may be representative respiratory rate data for users betweenthe ages of 18 and 47.

In some cases, the respiratory rate fluctuations may diminish with ageas menopause approaches. For example, the respiratory rate values 410 ofusers with ages 18 to 38 may be lower than the respiratory rate values410 of users with ages 38 to 47. The system may determine the userfertility prediction based on the age of the user, the respiratory ratevalues 410 of the user, or both.

In some cases, the system (e.g., ring 104, user device 106, server 110)may receive physiological data associated with a user from a wearabledevice. The physiological data may include at least respiratory ratedata. The system may determine a time series of a plurality ofrespiratory rate values 410 taken over a plurality of days based on thereceived respiratory rate data. With reference to timing diagram 400-b,the plurality of days may be an example of 28 days. For example, thetiming diagram 400-b may include respiratory rate values 410 throughouta menstrual cycle.

The system may process original time series respiratory rate data (e.g.,respiratory rate values 410) to determine the user fertility prediction.The respiratory rate values 410 may be continuously collected by thewearable device. The physiological measurements may be takencontinuously throughout the day and/or night. For example, in someimplementations, the ring may be configured to acquire physiologicaldata (e.g., respiratory rate data, MET data, and the like) continuouslyin accordance with one or more measurement periodicities throughout theentirety of each day/sleep day. In other words, the ring maycontinuously acquire physiological data from the user without regard to“trigger conditions” for performing such measurements.

In some implementations, the system may determine the user fertilityprediction by observing a user's relative respiratory rate for manydays. For example, the system may determine that the receivedrespiratory rate data (e.g., respiratory rate values 410) satisfies athreshold respiratory rate for the user for at least a portion of theplurality of days. In such cases, the system may determine the userfertility prediction in response to determining that the receivedrespiratory rate data satisfies the threshold respiratory rate for theuser. For example, the system may identify that the user's respiratoryrate begins to rise after 10 days of the menstrual cycle (e.g., aroundday 10 of the menstrual cycle) and begins to decrease after 25 days ofthe menstrual cycle (e.g., around day 25 of the menstrual cycle). Insome examples, the system may identify the respiratory rate values 410after determining the time series, and identify the thresholdrespiratory rate for the user.

In some examples, the system may determine that the received respiratoryrate data (e.g., respiratory rate values 410) exceeds (e.g., is greaterthan) a threshold respiratory rate for the user for at least a portionof the plurality of days. In such cases, the system may determine theuser fertility prediction in response to determining that the receivedrespiratory rate data exceeds the threshold respiratory rate for theuser. For example, the system may determine that the user is lessfertile or experiencing declining fertility based on determining thatthe received respiratory rate data exceeds the threshold respiratoryrate for the user. In such cases, users ages 38-47 may experienceincreased respiratory rates as compared to users ages 18-37. Therespiratory rate fluctuations may monotonically diminish with increasingage as menopause approaches. For example, estrogen fluctuations maydiminish as users approach menopause, and estrogen levels may beassociated with changes in cardiac and respiratory features. In suchcases, a decrease in fluctuations in estrogen (and other) markers mayindicate declining fertility and approaching menopause.

In some cases, one or more physiological measurements may be combined todetermine the user fertility prediction. In such cases, identifying theuser fertility prediction may be based on one physiological measurementor a combination of physiological measurements (e.g., temperature data,heart rate data, respiratory rate data, HRV data). For example, theuser's respiratory rate data in combination with the user's temperaturedata may be an indicator that may characterize the user's fertility. Insome examples, one or more physiological measurements may be combined todisprove the fertility prediction. If the system determines that thereceived temperature data is greater than the threshold temperature forthe user but the received respiratory rate data still aligns with thethreshold respiratory rate for the user, the system may determine thatthe determined user fertility prediction is invalid or at least lessaccurate than if both the temperate and respiratory rate deviate fromtheir thresholds.

In some cases, the system may determine that the received respiratoryrate data (e.g., respiratory rate values 410) is less than a thresholdrespiratory rate for the user for at least a portion of the plurality ofdays. In such cases, the system may determine the user fertilityprediction in response to determining that the received respiratory ratedata is less than the threshold respiratory rate for the user. Forexample, the system may determine that the user is more fertile orexperiencing increasing fertility based on determining that the receivedrespiratory rate data is less than the threshold respiratory rate forthe user. In such cases, users ages 18-37 may experience decreasedrespiratory rates as compared to users ages 38-47.

In some examples, one or more physiological measurements may be combinedto approve or validate the user fertility prediction. In some cases, theuser's respiratory rate data may confirm (e.g., provide a definitiveindication of or better prediction of) the user's fertility in light ofthe user's temperature data. For example, if the system determines thatthe received respiratory rate data exceeds the threshold respiratoryrate for the user and that the received temperature data exceeds thethreshold temperature for the user, the system may validate or determinethe user fertility prediction with greater accuracy and precision thanif one of the respiratory rate data or temperature data exceeds thethreshold.

The system may identify one or more slopes of the time series of therespiratory rate values 410. For example, the system may identify one orslopes of the time series of the plurality of respiratory rate values410 after determining the time series. The system may determine the userfertility prediction based on identifying the one or more slopes of thetime series. The one or more slopes may include positive slopes,negative slopes, or both.

In some cases, the system may determine, or estimate, the respiratoryrate maximum and/or minimum for a user after determining the time seriesof the respiratory rate values 410 for the user collected via the ring.The system may identify the one or more slopes of the time series of theplurality of respiratory rate values 410 based on determining themaximum and/or minimum. In some cases, calculating the differencebetween the maximum and minimum may determine the slope. In otherexamples, identifying the one or more slopes of the time series of theplurality of respiratory rate values 410 may be in response to computinga derivative of the original time series respiratory rate data (e.g.,respiratory rate values 410).

As described in further detail herein, the system may be configured totrack menstrual cycles, ovulation, pregnancy, fertility, and the like.In some cases, the user's respiratory rate pattern throughout the dayand/or night may be an indicator that may characterize fertility. Forexample, respiratory rate during the day and/or night may determine theuser fertility prediction. As such, the timing diagram 400-b illustratesa relationship between a user's respiratory rate data and a time (e.g.,over a plurality of days).

The timing diagram 400-c illustrates a relationship between a user'sheart rate data and a time (e.g., over a menstrual cycle). In thisregard, the solid curved line illustrated in the timing diagram 400-cmay be understood to refer to the “heart rate values 415.” The user'sheart rate values 415 may be relative to a baseline heart rate. Themenstrual cycle bins may be representative of a day of the user'smenstrual cycle. In some cases, the heart rate values 415 may berepresentative heart rate data for users between the ages of 18 and 47.

In some cases, the heart rate fluctuations may diminish with age asmenopause approaches. For example, the heart rate values 415 of userswith ages 18 to 38 may be lower than the heart rate values 415 of userswith ages 38 to 47. The system may determine the user fertilityprediction based on the age of the user, the heart rate values 415 ofthe user, or both.

In some cases, the system (e.g., ring 104, user device 106, server 110)may receive physiological data associated with a user from a wearabledevice. The physiological data may include at least heart rate data. Thesystem may determine a time series of a plurality of heart rate values415 taken over a plurality of days based on the received heart ratedata. With reference to timing diagram 400-c, the plurality of days maybe an example of 28 days. For example, the timing diagram 400-c mayinclude heart rate values 415 throughout a menstrual cycle.

The system may process original time series heart rate data (e.g., heartrate values 415) to determine the user fertility prediction. The heartrate values 415 may be continuously collected by the wearable device.The physiological measurements may be taken continuously throughout theday and/or night. For example, in some implementations, the ring may beconfigured to acquire physiological data (e.g., heart rate data, METdata, and the like) continuously in accordance with one or moremeasurement periodicities throughout the entirety of each day/sleep day.In other words, the ring may continuously acquire physiological datafrom the user without regard to “trigger conditions” for performing suchmeasurements.

In some implementations, the system may determine the user fertilityprediction by observing a user's relative heart rate for many days. Forexample, the system may determine that the received heart rate data(e.g., heart rate values 415) satisfies a threshold heart rate for theuser for at least a portion of the plurality of days. In such cases, thesystem may determine the user fertility prediction in response todetermining that the received heart rate data satisfies the thresholdheart rate for the user. For example, the system may identify that theuser's heart rate begins to rise after 4 days of the menstrual cycle(e.g., around day 4 of the menstrual cycle) and begins to decrease after22 days of the menstrual cycle (e.g., around day 22 of the menstrualcycle). In some examples, the system may identify the heart rate values415 after determining the time series, and identify the threshold heartrate for the user.

The system may determine that the received heart rate data (e.g., heartrate values 415) exceeds (e.g., is greater than) a threshold heart ratefor the user for at least a portion of the plurality of days. In suchcases, the system may determine the user fertility prediction inresponse to determining that the received heart rate data exceeds thethreshold heart rate for the user. For example, the system may determinethat the user is less fertile or experiencing declining fertility basedon determining that the received heart rate data exceeds the thresholdheart rate for the user. In such cases, users ages 38-47 may experienceincreased heart rates as compared to users ages 18-37. The heart ratefluctuations may monotonically diminish with increasing age as menopauseapproaching. For example, estrogen fluctuations may diminish as usersapproach menopause, and estrogen levels may be associated with changesin cardiac and respiratory features. In such cases, a decrease influctuations in estrogen (and other) markers may indicate decliningfertility and approaching menopause.

In some cases, the user's respiratory rate data in combination with theuser's heart rate data may be an indicator that may characterize theuser's fertility. For example, one or more physiological measurementsmay be combined to disprove the fertility prediction. If the systemdetermines that the received heart rate data is greater than thethreshold heart rate for the user but the received respiratory rate datastill aligns with the threshold respiratory rate for the user, thesystem may determine that the determined user fertility prediction isinvalid or at least less accurate than if both the heart rate andrespiratory rate deviate from their thresholds.

The system may determine that the received heart rate data (e.g., heartrate values 415) is less than a threshold heart rate for the user for atleast a portion of the plurality of days. In such cases, the system maydetermine the user fertility prediction in response to determining thatthe received heart rate data is less than the threshold heart rate forthe user. For example, the system may determine that the user is morefertile or experiencing increasing fertility based on determining thatthe received heart rate data is less than the threshold heart rate forthe user. In such cases, users ages 18-37 may experience decreased heartrates as compared to users ages 38-47.

In some examples, one or more physiological measurements may be combinedto approve or validate the user fertility prediction. In some cases, theuser's respiratory rate data may confirm (e.g., provide a definitiveindication of or better prediction of) the user's fertility in light ofthe user's heart rate data. For example, if the system determines thatthe received respiratory rate data exceeds the threshold respiratoryrate for the user and that the received heart rate data exceeds thethreshold heart rate for the user, the system may validate or determinethe user fertility prediction with greater accuracy and precision thanif one of the respiratory rate data or heart rate data exceeds thethreshold. In such cases, the system may determine that the user isexperiencing an increase in fertility based on the received respiratoryrate data being less than the threshold respiratory rate for the userand that the received heart rate data is less than the threshold heartrate for the user.

The system may identify one or more slopes of the time series of theheart rate values 415. For example, the system may identify one or moreslopes of the time series of the plurality of heart rate values 415after determining the time series. The system may determine the userfertility prediction based on identifying the one or more slopes of thetime series. The one or more slopes may include positive slopes,negative slopes, or both.

In some cases, the system may determine, or estimate, the heart ratemaximum and/or minimum for a user after determining the time series ofthe heart rate values 415 for the user collected via the ring. Thesystem may identify the one or more slopes of the time series of theplurality of heart rate values 415 based on determining the maximumand/or minimum. In some cases, calculating the difference between themaximum and minimum may determine the slope. In other examples,identifying the one or more slopes of the time series of the pluralityof heart rate values 415 may be in response to computing a derivative ofthe original time series heart rate data (e.g., heart rate values 415).

As described in further detail herein, the system may be configured totrack menstrual cycles, ovulation, pregnancy, fertility, and the like.In some cases, the user's heart rate pattern throughout the day and/ornight may be an indicator that may characterize fertility. For example,heart rate during the day and/or night may determine the user fertilityprediction. As such, the timing diagram 400-c illustrates a relationshipbetween a user's heart rate data and a time (e.g., over a plurality ofdays).

The timing diagram 400-d illustrates a relationship between a user's HRVdata and a time (e.g., over a menstrual cycle). In this regard, thesolid curved line illustrated in the timing diagram 400-d may beunderstood to refer to the “HRV values 420.” The user's HRV values 420may be relative to a baseline HRV. The menstrual cycle bins may berepresentative of a day of the user's menstrual cycle. In some cases,the HRV values 420 may be representative HRV data for users between theages of 18 and 47.

In some cases, the heart rate fluctuations may diminish with age asmenopause approaches. For example, the HRV values 420 of users with ages18 to 38 may be greater than the HRV values 420 of users with ages 38 to47. In some cases, the HRV values 420-a may be representative of HRVdata for a user age 18 to 31, the HRV values 420-b may be representativeof HRV data for a user age 31 to 35, and the HRV values 420-c may berepresentative of HRV data for a user age 35 to 38. In some cases, theHRV values 420-d may be representative of HRV data for a user age 38 to41, the HRV values 420-e may be representative of HRV data for a userage 41 to 44, and the HRV values 420-f may be representative of HRV datafor a user age 44 to 47. The system may determine the user fertilityprediction based on the age of the user, the HRV values 420 of the user,or both.

In some cases, the system (e.g., ring 104, user device 106, server 110)may receive physiological data associated with a user from a wearabledevice. The physiological data may include at least HRV data. The systemmay determine a time series of a plurality of HRV values 420 taken overa plurality of days based on the received HRV data. With reference totiming diagram 400-d, the plurality of days may be an example of 28days. For example, the timing diagram 400-d may include HRV values 420throughout a menstrual cycle.

The system may process original time series HRV data (e.g., HRV values420) to determine the user fertility prediction. The HRV values 420 maybe continuously collected by the wearable device. The physiologicalmeasurements may be taken continuously throughout the day and/or night.For example, in some implementations, the ring may be configured toacquire physiological data (e.g., HRV data, MET data, and the like)continuously in accordance with one or more measurement periodicitiesthroughout the entirety of each day/sleep day. In other words, the ringmay continuously acquire physiological data from the user without regardto “trigger conditions” for performing such measurements.

In some implementations, the system may determine the user fertilityprediction by observing a user's relative HRV for many days. Forexample, the system may determine that the received HRV data (e.g., HRVvalues 420) satisfies a threshold HRV for the user for at least aportion of the plurality of days. In such cases, the system maydetermine the user fertility prediction in response to determining thatthe received HRV data satisfies the threshold HRV for the user. In somecases, the system may identify that the user's HRV values begins todecrease after 4 days of the menstrual cycle and begins to increaseafter 19 days of the menstrual cycle. In some examples, the system mayidentify the HRV values 420 after determining the time series, andidentify the threshold HRV for the user.

The system may determine that the received HRV data (e.g., HRV values420) exceeds (e.g., is greater than) a threshold HRV for the user for atleast a portion of the plurality of days. In such cases, the system maydetermine the user fertility prediction in response to determining thatthe received HRV data exceeds the threshold HRV for the user. Forexample, the system may determine that the user is fertile based ondetermining that the received HRV data exceeds the threshold HRV for theuser.

The system may determine that the received HRV data (e.g., HRV values420) is less than a threshold HRV for the user for at least a portion ofthe plurality of days. In such cases, the system may determine the userfertility prediction in response to determining that the received HRVdata is less than the threshold HRV for the user. For example, thesystem may determine that the user is less fertile or experiencingdecreasing fertility based on determining that the received HRV data isless than the threshold HRV for the user. In such cases, users ages38-47 may experience decreased HRV data as compared to users ages 18-37.The HRV fluctuations may monotonically diminish with increasing age asmenopause approaching.

The user's respiratory rate data in combination with the user's HRV datamay be an indicator that may characterize the user's fertility. In somecases, one or more physiological measurements may be combined todisprove the fertility prediction. For example, if the system determinesthat the HRV data is less than the threshold HRV for the user but thereceived respiratory rate data still aligns with the thresholdrespiratory rate for the user, the system may determine that thedetermined user fertility prediction is invalid than if both the HRV andrespiratory rate deviate from their thresholds. In some examples, if thesystem determines that the received respiratory rate data exceeds thethreshold respiratory rate for the user and that the received HRV datais less than the threshold heart rate for the user, the system mayvalidate or determine the user fertility prediction with greateraccuracy and precision than if one of the respiratory rate data or HRVdata deviates from the threshold. In such cases, the system maydetermine that the user is experiencing an increase in fertility basedon the received respiratory rate data being less than the thresholdrespiratory rate for the user and that the received HRV data is greaterthan the threshold HRV for the user.

The system may identify one or more slopes of the time series of the HRVvalues 420. For example, the system may identify one or slopes of thetime series of the plurality of HRV values 420 after determining thetime series. The system may determine the user fertility predictionbased on identifying the one or more slopes of the time series. The oneor more slopes may include positive slopes, negative slopes, or both.

In some cases, the system may determine, or estimate, the HRV maximumand/or minimum for a user after determining the time series of the HRVvalues 420 for the user collected via the ring. The system may identifythe one or more slopes of the time series of the plurality of HRV values420 based on determining the maximum and/or minimum. In some cases,calculating the difference between the maximum and minimum may determinethe slope. In other examples, identifying the one or more slopes of thetime series of the plurality of HRV values 420 may be in response tocomputing a derivative of the original time series HRV data (e.g., HRVvalues 420).

As described in further detail herein, the system may be configured totrack menstrual cycles, ovulation, pregnancy, fertility, and the like.In some cases, the user's HRV pattern throughout the day and/or nightmay be an indicator that may characterize fertility. For example, HRVduring the day and/or night may determine the user fertility prediction.As such, the timing diagram 400-d illustrates a relationship between auser's HRV data and a time (e.g., over a plurality of days).

FIG. 5 illustrates an example of a timing diagram 500 that supportsfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure. The timing diagram500 may implement, or be implemented by, aspects of the system 100,system 200, system 300, or a combination thereof. For example, in someimplementations, the timing diagram 500 may be displayed to a user viathe GUI 275 of the user device 106, as shown in FIG. 2.

As described in further detail herein, the system may be configured todetermine a plurality of menstrual cycle length parameters and determinethe user fertility prediction based on the menstrual cycle lengthparameters. In some cases, the user's body temperature patternthroughout the day and night may be an indicator that may characterizemenstrual cycle length parameters. For example, skin temperature duringthe day and night may determine the menstrual cycle length parameters.As such, the timing diagram 500 illustrates a relationship between auser's temperature data and a time (e.g., over a plurality of months).

In this regard, the solid curved line illustrated in the timing diagram500 may be understood to refer to the “temperature values 505.” Thedashed vertical line illustrated in the timing diagram 500 may beunderstood to refer to “ovulation 510.” The dotted vertical lineillustrated in the timing diagram 500 may be understood to refer to“period 515.” The user's temperature values 505 may be relative to abaseline temperature. The timing diagram 500 may illustrate a user withthree periods 515, three ovulations 510, three luteal phases 520, andthree follicular phases 525. In such cases, the timing diagram 500 mayillustrate a user with at least three menstrual cycles.

In some cases, the system (e.g., ring 104, user device 106, server 110)may receive physiological data associated with a user from a wearabledevice. The physiological data may include at least temperature data.The system may determine a time series of temperature values 505 takenover a plurality of days based on the received temperature data. Withreference to timing diagram 500, the plurality of days may be an exampleof three months. The system may process original time series temperaturedata (e.g., temperature values 505) to determine the menstrual cyclelength parameters. In some cases, the time series may include aplurality of events tagged by the user in the system. For example, thetime series may include ovulations 510 and periods 515. In some cases,the ovulations 510 and periods 515 may be determined by the system basedon physiological data continuously collected by the system. In somecases, the ovulations 510 and periods 515 may be manually input (e.g.,tagged) into the user device.

The temperature values 505 may be continuously collected by the wearabledevice. The physiological measurements may be taken continuouslythroughout the day and/or night. For example, in some implementations,the ring may be configured to acquire physiological data (e.g.,temperature data, sleep data, heart rate, HRV data, respiratory ratedata, sleep data, MET data, and the like) continuously in accordancewith one or more measurement periodicities throughout the entirety ofeach day/sleep day. In other words, the ring may continuously acquirephysiological data from the user without regard to “trigger conditions”for performing such measurements. In some cases, continuous temperaturemeasurement at the finger may capture temperature fluctuations (e.g.,small or large fluctuations) that may not be evident in coretemperature. For example, continuous temperature measurement at thefinger may capture minute-to-minute or hour-to-hour temperaturefluctuations that provide additional insight that may not be provided byother temperature measurements elsewhere in the body or if the user weremanually taking their temperature once per day.

In some implementations, the system may determine the menstrual cyclelength parameters by observing a user's relative body temperature formany days and marking the decrease or increase in temperature relativeto a baseline, which may indicate a menstrual cycle length parameter.The menstrual cycle length parameters may include at least one of anaverage menstrual cycle length, a standard deviation menstrual cyclelength, an average follicular phase 525 length, an average luteal phase520 length, a range of menstrual cycle lengths, a quantity ofanovulatory cycles, or a combination thereof. In some cases, the systemmay transmit an alert when a user's follicular phase 525 or luteal phase520 length is shorter or longer than her previous cycles.

The average cycle length, average follicular phase 525 length, averageluteal phase 520 length, and range of menstrual cycle lengths mayinclude a duration of time (e.g., time span) including at least a day, aplurality of days, a week, a plurality of weeks, or a month. In suchcases, the indication of the average cycle length, average follicularphase 525 length, average luteal phase 520 length, and range ofmenstrual cycle lengths may each include a start date and an end date.

The system may determine menstrual cycle length parameters in the timeseries of the temperature values 505 based on identifying one or morelocal maximum 535 of the time series of the plurality of temperaturevalues 505. The one or more local maximum 535 of the time series of theplurality of temperature values 505 may be identified in response todetermining the time series. For example, the system may calculate aduration between a first local maximum 535-a of the one or more localmaximum 535 and a second local maximum 535-b of the one or more localmaximum 535 based on the identifying. In such cases, the system maydetermine the menstrual cycle length parameters in response tocalculating the duration. For example, the duration between the firstlocal maximum 535-a and the second local maximum 535-b may be used todetermine an average menstrual cycle length, a standard deviationmenstrual cycle length, an average follicular phase 525 length, or acombination thereof.

In some cases, the system may identify one or more positive slopes 530of the time series of the plurality of temperature values 505 inresponse to determining the time series. In such cases, the system maydetermine the menstrual cycle length parameters in response toidentifying the one or more positive slopes. For example, the identifiedone or more positive slopes 530 may be used to determine ovulation 510,the start of the luteal phase 520, the end of the follicular phase 525,or a combination thereof.

In some cases, the system may identify one or more negative slopes ofthe time series of the plurality of temperature values 505 in responseto determining the time series. In such cases, the system may determinethe menstrual cycle length parameters in response to identifying the oneor more negative slopes. For example, the identified one or morenegative slopes may be used to determine period 515, the end of theluteal phase 520, the start of the follicular phase 525, or acombination thereof. In such cases, identifying one or more negative andpositive slopes of the time series may determine the average follicularphase 525 length, the average luteal phase 520 length, or both.

The system may identify the one or more slopes of the time series of theplurality of temperature values 505 based on determining the temperaturemaximum and/or temperature minimum. In some cases, calculating thedifference between the maximum and minimum may determine the slope. Inother examples, identifying the one or more slopes of the time series ofthe plurality of temperature values 505 may be in response to computinga derivative of the original time series temperature data (e.g.,temperature values 505).

As described in further detail herein, the system may be configured totrack menstrual cycles, ovulation, pregnancy, fertility, and the like.In some cases, the user's body temperature pattern throughout the daymay be an indicator that may characterize fertility. For example, skintemperature during the day may determine the menstrual cycle lengthparameters. As such, the timing diagram 500 illustrates a relationshipbetween a user's temperature data and a time (e.g., over a plurality ofmonths).

FIG. 6 illustrates an example of a GUI 600 that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure. The GUI 600 may implement, or beimplemented by, aspects of the system 100, system 200, system 300,timing diagrams 400, timing diagram 500, or any combination thereof. Forexample, the GUI 600 may be an example of a GUI 275 of a user device 106(e.g., user device 106-a, 106-b, 106-c) corresponding to a user 102.

In some examples, the GUI 600 illustrates a series of application pages605 which may be displayed to a user via the GUI 600 (e.g., GUI 275illustrated in FIG. 2). The server of the system may cause the GUI 600of the user device (e.g., mobile device) to display inquiries of whetherthe user activates the fertility mode and wants to track their fertility(e.g., via application page 605). In such cases, the system may generatea personalized cycle tracking experience on the GUI 600 of the userdevice to determine the user's fertility prediction based on thecontextual tags and user questions.

Continuing with the examples above, prior to determining the fertilityprediction, the user may be presented with an application page uponopening the wearable application. The application page 605 may display arequest to activate the fertility mode and enable the system to tracktheir fertility. In such cases, the application page 605 may display aninvitation card where the users are invited to enroll in the fertilitytracking applications. The application page 605 may display a prompt tothe user to verify whether the fertility may be tracked or dismiss themessage if the fertility is not tracked. The system may receive anindication of whether the user selects to opt-in to tracking theirfertility or opt-out to tracking their fertility.

The user may be presented with an application page 605 upon selecting“yes” to tracking their fertility. The application page 605 may displaya prompt to the user to verify the main reason to track their fertility(e.g., ovulation, pregnancy, etc.). In such cases, the application page605 may prompt the user to confirm the intent of tracking theirfertility. For example, the system may receive, via the user device, aconfirmation of the intended use of the tracking system.

In some cases, the user may be presented with an application page 605upon confirming the intent. The application page 605 may display aprompt to the user to verify the average cycle length (e.g., durationbetween a first day of a first menstrual cycle and a first day of asecond menstrual cycle). In some cases, the application page 605 maydisplay a prompt to the user to indicate whether the user experiencesirregular cycles in which an average cycle length may not be determined.For example, the system may receive, via the user device, a confirmationof the average cycle length. In some cases, the application page 605 maydisplay a prompt to the user to indicate an age of the user, a weight ofthe user, a lifestyle summary of the user, a type of fertility treatmentexperienced by the user, a quantity of miscarriages experienced by theuser, or a combination thereof.

The user may be presented with an application page 605 upon inputtingthe information. The application page 605 may display a prompt to theuser to verify the last cycle start date (e.g., a first day of the mostrecent menstrual cycle). The application page 605 may display a promptto the user to indicate whether the user may be unable to identify thelast cycle start date. For example, the system may receive, via the userdevice, a confirmation of the last cycle start date.

In some cases, the user may be presented with an application page 605upon confirming the last cycle start date. The application page maydisplay a prompt to the user to verify whether the user uses hormonalcontraceptives in use. For example, the system may receive, via the userdevice, a confirmation of whether hormonal contraceptives are in use.Upon confirming that hormonal contraceptives are not in use, the usermay be presented with a GUI 600 that may be further shown and describedwith reference to application page 605.

The server of system may cause the GUI 600 of the user device (e.g.,mobile device) to display the user fertility prediction (e.g., viaapplication page 605). In such cases, the system may output the userfertility prediction on the GUI 600 of the user device to indicate auser's fertility score 630.

Continuing with the example above, upon determining the user fertilityprediction, the user may be presented with the application page 605-aupon opening the wearable application. As shown in FIG. 6, theapplication page 605 may display the user fertility prediction viamessage 620. In such cases, the application page 605 may include themessage 620 on the home page. In cases where a user's fertilityprediction may be identified, as described herein, the server maytransmit a message 620 to the user, where the message 620 is associatedwith the user fertility prediction for the user. In some cases, theserver may transmit a message 620 to a clinician, a fertilityspecialist, a care-taker, a partner of the user, or a combinationthereof. In such cases, the system may present application page 605 onthe user device associated with the clinician, the fertilityspecialists, the care-taker, the partner, or a combination thereof.

For example, the user may receive message 620, which may include a timeinterval during which the determined user fertility prediction is validfor, a probability of becoming pregnant within the time interval basedon the determined user fertility prediction, an indication ofphysiological and behavioral indicators that contribute to thedetermined user fertility prediction, educational content associatedwith the determined user fertility prediction, recommendations toimprove the determined user fertility prediction, and the like. Forexample, the probability of becoming pregnant within the time intervalbased on the determined user fertility prediction may be an example ofthe score 630. As described herein, the indication of physiological andbehavioral indicators that contribute to the determined user fertilityprediction may include at least temperature data, heart rate data, HRVdata, respiratory rate data, ovarian reserve contributors, menstrualcycle length contributors, a user's age, a user's weight, a user'sexercise routine and/or diet, a user's sleep patterns, or a combinationthereof. As described herein, many of these physiological and behavioralindicators may be directly measured and/or derived from measurementstaking for a wearable device. The messages 620 may beconfigurable/customizable, such that the user may receive differentmessages 620 based on the user fertility prediction, as describedpreviously herein.

Additionally, in some implementations, the application page 605 maydisplay one or more scores (e.g., Sleep Score, Readiness Score, etc.)for the user for the respective day. The application pages 605 maydisplay a fertility card such as a “determined user fertility predictioncard” which indicates that the user fertility prediction has beenrecorded. In some cases, the user fertility prediction may be used toupdate (e.g., modify) one or more scores associated with the user (e.g.,Sleep Score, Readiness Score, Activity Score, etc.). That is, dataassociated with the user fertility prediction may be used to update thescores for the user for the following calendar day after which the userfertility prediction was determined.

In some cases, the Readiness Score may be updated based on the userfertility prediction. For example, the Readiness Score may indicate tothe user to “pay attention” based on fertility predictions. If theReadiness Score changes for the user, the system may implement arecovery mode for users whose symptoms may be severe and may benefitfrom adjusted activity and readiness guidance for a couple of days. Inother examples, the Readiness Score may be updated based on the SleepScore and fertility predictions. However, the system may determine theuser fertility prediction and may adjust the Readiness Score and/orSleep Score.

In some cases, the messages 620 displayed to the user via the GUI 600 ofthe user device may indicate how the fertility prediction affected theoverall scores (e.g., overall Readiness Score, Sleep Score, ActivityScore, etc.) and/or the individual contributing factors. For example, amessage may indicate “It looks like your body is under strain right now,but if you're feeling ok, doing a light or medium intensity exercise canhelp your body battle the symptoms.”

In cases where the fertility prediction is determined, the messages 620may provide suggestions for the user in order to improve their generalhealth. For example, the message may indicate “If you feel really low onenergy, why not switch to rest mode for today,” or “Since you arefeeling fatigued, devote today for rest.” In such cases, the messages620 displayed to the user may provide targeted insights to help the useradjust their lifestyle. In such cases, accurately determining the userfertility prediction may increase the accuracy and efficiency of theReadiness Score and Activity Scores.

In cases where the user dismisses the prompt (e.g., alert 610) onapplication page 605-a, the prompt may disappear, and the user may inputan indication via user input 625 at a later time. In some cases, thesystem may display via message 620 a prompt asking the user if the useris pregnant or suggests switching to an alternative mode (e.g.,pregnancy mode, rest mode) or deactivating fertility mode. In suchcases, the system may recommend the user switch from fertility trackingmode to a pregnancy mode or rest mode based on user input 625. In somecases, the system may receive, via the user device, an indicationcomprising an age of the user, a weight of the user, a lifestyle summaryof the user, a type of fertility treatment experienced by the user, aquantity of miscarriages experienced by the user, or a combinationthereof. In such cases, determining the user fertility prediction may bebased on receiving the indication.

The application page 605 may indicate one or more parameters of thefertility prediction, including a temperature, heart rate, HRV,respiratory rate, and the like experienced by the user via the graphicalrepresentation 615. The graphical representation 615 may be an exampleof the timing diagram 500, as described with reference to FIG. 5.

In some cases, the user may log symptoms via user input 625. Forexample, the system may receive user input (e.g., tags) to log symptomsassociated with the phase of their menstrual cycle. The system mayrecommend tags to the user based on user history and the user fertilityprediction. In some cases, the system may cause the GUI 600 of the userdevice to display symptom tags based on determining the user fertilityprediction.

Application page 605-a may also include message 620 that includesinsights, recommendations, and the like associated with the userfertility prediction. The server of system may cause the GUI 600 of theuser device to display a message 620 associated with the user fertilityprediction. The user device may display recommendations and/orinformation associated with the user fertility prediction via message620. As noted previously herein, an accurately determined user fertilityprediction may be beneficial to a user's overall health. In someimplementations, the user device and/or servers may generate alerts 610associated with the user fertility prediction which may be displayed tothe user via the GUI 600 (e.g., application page 605-a). In particular,messages 620 generated and displayed to the user via the GUI 600 may beassociated with one or more characteristics (e.g., timing) of the userfertility prediction.

In some implementations, the system may provide additional insightregarding the user's fertility prediction. For example, the applicationpages 605 may indicate one or more physiological parameters (e.g.,contributing factors) which resulted in the user's fertility prediction.In other words, the system may be configured to provide some informationor other insights regarding the user fertility prediction. Personalizedinsights may indicate aspects of collected physiological data (e.g.,contributing factors within the physiological data) which were used togenerate the messages associated with the user fertility prediction.

In some implementations, the system may be configured to receive userinputs 625 regarding user fertility prediction in order to trainclassifiers (e.g., supervised learning for a machine learningclassifier) and improve user fertility prediction techniques. Forexample, the user may receive user input 625, and these user inputs 625may then be input into the classifier to train the classifier. In otherwords, the user inputs 625 may be used to validate, or confirm, the userfertility prediction.

Upon determining the user fertility prediction on application page 605,the GUI 600 may display a calendar view that may indicate a current datethat the user is viewing application page 605, a date range includingthe days when the user is fertile, a date range including the days whenthe user in infertile, or a combination thereof. For example, the daterange may encircle the calendar days using a dashed line configurationand, the current date may encircle the calendar day. The calendar viewmay also include a message including the current calendar day andindication of the day of the user's menstrual cycle (e.g., that the useris on day 14 of their menstrual cycle).

Upon determining the user fertility prediction, the GUI 600 may displayapplication page 605-b in which the fertility score 630 associated withthe determined user fertility prediction is displayed. For example, thesystem may provide users with a fertility score 630 and an explanationof what physiological and behavioral indicators represent the biggestcontributors to that fertility score 630.

In some implementations, the application page 605-b may provide links tofurther web-based blogs and information about how to interpret theseindicators or guidelines for making these insights actionable. In someimplementations, the system may determine physiological features fromacquired physiological data and, based on the acquired data, provideusers with an estimation of their fertility via fertility score 630. Forexample, an estimation may include a fertility score 630 from 0 to 100,where 0 corresponds to infertile and 100 corresponds to very fertile. Insome implementations, the fertility score 630 may reflect a probabilityof getting pregnant within a prescribed and clinically relevant amountof time, such as 6 or 12 months. In some implementations, the fertilityscore 630 may reflect an age-adjusted probability of getting pregnant(or age and BMI-adjusted), given that age may be a deterministicpredictor. Some controllable behavioral factors may affect the fertilityscore 630, such as sleep, exercise, weight, nutrition, stress/meditativepractices, and some environmental exposures. In some implementations,the fertility score 630 may be additionally tailored to the type offertility treatment a user has engaged in. For example, the probabilitymay increase or decrease depending on whether a user is receivingvarious in vitro fertilization (IVF) protocols, intrauterineinsemination (IUI) protocols, or tracking her luteinizing hormone (LH)surge with at-home tests. The probability may also depend on the numberof previous miscarriages or unsuccessful attempts at pregnancy.

In some cases, the fertility score 630 may be the weight sum of a set offertility contributors 635. The fertility contributors 635 may beinclude two main categories of ovarian reserve contributors andmenstrual cycle length contributors. Ovarian reserve contributors may beestimated using machine learning to predict a user's FSH levels, AFC,AMH level. The menstrual cycle length contributors may use the fertilitycycle mapping algorithm to track shifts in cycle phase lengths of auser's reproductive lifespan. If the system alerts a user, via alert610, that the user may be at risk for any of these fertilitycontributors 635, the user may be prompted to confirm this in afertility clinic.

The fertility contributors 635 may include ovarian reserve estimation.The assessment of ovarian reserve may currently be the state-of-the-arttest used to counsel IVF patients on their likelihood of having a livebirth if they choose to proceed with IVF treatment. Ovarian reserve mayalso be used to titrate ovarian stimulation medications in IVF tominimize safety risks. In some cases, ovarian reserve assessment may beused by fertility clinics to estimate current and future fertilitypotential for patients. The system may display the ovarian reserveestimation to the user via application page 605-b.

In some cases, estimators may be used in fertility clinics to assessovarian reserve that comprises the ovarian reserve contributors. Theovarian reserve contributors may be an example of Basal FSH levels, AFClevels, and AMH levels. In such cases, the system may estimate each ofthese ovarian reserve contributors to calculate the user's overallovarian reserve estimation.

The fertility contributors 635 may include basal FSH estimation FSH.Pregnancy and live birth rates may decline with increasing FSH andadvancing age. In some systems, basal FSH levels may be measured viaserum (i.e. a blood draw). However, basal FSH testing may be limited bywide intercycle variability, which weakens its reliability. The systemmay be able to provide improvements to estimating FSH levels bycontinuously monitoring the physiological data of the user and thus arenot inhibited by intercycle variability, and alerting users to droppinglevels far earlier than a user would typically see with FSH measurementsfrom a clinic.

The basal FSH levels may be updated once per menstrual cycle in theearly follicular phase and may be estimated using a machine learningmodel. The input to the model may be features that capture physiologicalfluctuations across the user's previous menstrual cycle relative to allpast menstrual cycles, including temperature, heart rate, HRV, andrespiratory rate, as described with reference to FIG. 4. The output ofthe model may be trained on a database of ground truth basal FSH levels.The system may be able to estimate basal FSH levels with higher accuracybecause FSH levels may be known to be reflected in cardiac andrespiratory metrics.

The fertility contributors 635 may include AFC levels. The AFC levelsmay have a high predictive value in assessing a user's likelihood of IVFsuccess. The AFC levels may also be used to inform titration levels forovarian stimulation. The estimated AFC levels may be updated once permenstrual cycle at the beginning of the cycle and may be estimated usinga machine learning model. The input to the model may be features thatcapture physiological fluctuations across the user's previous menstrualcycle relative to all past menstrual cycles, including temperature,heart rate, HRV, and respiratory rate, as described with reference toFIG. 4. The output of the model may be trained on a database of groundtruth AFC levels. The system may be able to estimate AFC levels withhigher accuracy because AFC levels may be known to be reflected incardiac and respiratory metrics.

In some cases, the fertility contributors 635 may include AMH levels.The AMH serum levels may be positively correlated with histologicallydetermined primordial follicle number and are negatively correlated withchronologic age. The AMH levels may be used for determining ovarianreserve. For example, the AMH levels may be a predictor of poor ovarianresponse to IVF and pregnancy outcomes. The AMH production may beindependent of FSH and thus these contributors combined may determineovarian reserve. The estimated AMH levels may be updated once permenstrual cycle at the beginning of the cycle and will be estimatedusing a machine learning model. The input to the model may be featuresthat capture physiological fluctuations across the user's previousmenstrual cycle relative to all past menstrual cycles, includingtemperature, heart rate, HRV, and respiratory rate, as described withreference to FIG. 4. The output of the model may be trained on adatabase of ground truth AMH levels. The system may be able to estimateAMH levels with higher accuracy because AMH levels may be known to bereflected in cardiac and respiratory metrics.

The fertility contributors 635 may include follicular length. In somecases, diminished ovarian reserve (DOR) may be associated with a shorterfollicular phase, and polycystic ovarian syndrome (PCOS) may beassociated with a longer follicular phase. The system may estimate thefollicular phase length, as described with reference to FIG. 5.

In some examples, the fertility contributors 635 may include luteallength. In some cases, when the length of the luteal phase decreases,the system may predict that the body is not producing enoughprogesterone, which is a predictor of a user's ability to become andstay pregnant. In such cases, low progesterone levels may be associatedwith higher chances of miscarriage. The system may estimate the lutealphase length, as described with reference to FIG. 5.

The fertility contributors 635 may include cycle regularity. The cycleregularity may be a factor in predicting infertility and menopauseonset. The system may estimate the menstrual cycle length, as describedwith reference to FIG. 5. In some cases, the system may estimate auser's average cycle length, cycle variability, or both and alert theuser, via alert 610, if a user's cycles are abnormally short or long,flag highly variable cycles, or a combination thereof.

In some cases, the system may indicate whether the fertilitycontributors 635, fertility score 630, or both are in a recommendedrange based on the user information (e.g., age, weight, lifestyle,fertility treatments, etc.). For example, the system may display whetherthe fertility contributors 635, fertility score 630, or both are “PayAttention,” “Good,” or “Optimal.”

FIG. 7 shows a block diagram 700 of a device 705 that supports fertilityprediction from wearable-based physiological data in accordance withaspects of the present disclosure. The device 705 may include an inputmodule 710, an output module 715, and a wearable application 720. Thedevice 705 may also include a processor. Each of these components may bein communication with one another (e.g., via one or more buses).

The input module 710 may provide a means for receiving information suchas packets, user data, control information, or any combination thereofassociated with various information channels (e.g., control channels,data channels, information channels related to illness detectiontechniques). Information may be passed on to other components of thedevice 705. The input module 710 may utilize a single antenna or a setof multiple antennas.

The output module 715 may provide a means for transmitting signalsgenerated by other components of the device 705. For example, the outputmodule 715 may transmit information such as packets, user data, controlinformation, or any combination thereof associated with variousinformation channels (e.g., control channels, data channels, informationchannels related to illness detection techniques). In some examples, theoutput module 715 may be co-located with the input module 710 in atransceiver module. The output module 715 may utilize a single antennaor a set of multiple antennas.

For example, the wearable application 720 may include a data acquisitioncomponent 725, a temperature data component 730, a cycle lengthcomponent 735, a fertility prediction component 740, a user interfacecomponent 745, or any combination thereof. In some examples, thewearable application 720, or various components thereof, may beconfigured to perform various operations (e.g., receiving, monitoring,transmitting) using or otherwise in cooperation with the input module710, the output module 715, or both. For example, the wearableapplication 720 may receive information from the input module 710, sendinformation to the output module 715, or be integrated in combinationwith the input module 710, the output module 715, or both to receiveinformation, transmit information, or perform various other operationsas described herein.

The data acquisition component 725 may be configured as or otherwisesupport a means for receiving physiological data associated with a userfrom a wearable device, the physiological data comprising at leasttemperature data. The temperature data component 730 may be configuredas or otherwise support a means for determining a time series of aplurality of temperature values taken over a plurality of days based atleast in part on the received temperature data, wherein the time seriescomprises a plurality of menstrual cycles for the user. The cycle lengthcomponent 735 may be configured as or otherwise support a means fordetermining a plurality of menstrual cycle length parameters associatedwith the plurality of menstrual cycles based at least in part on thereceived temperature data. The fertility prediction component 740 may beconfigured as or otherwise support a means for determining a userfertility prediction based at least in part on determining the pluralityof menstrual cycle length parameters. The user interface component 745may be configured as or otherwise support a means for generating amessage for display on a graphical user interface on a user device thatindicates the determined user fertility prediction.

FIG. 8 shows a block diagram 800 of a wearable application 820 thatsupports fertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure. The wearableapplication 820 may be an example of aspects of a wearable applicationor a wearable application 720, or both, as described herein. Thewearable application 820, or various components thereof, may be anexample of means for performing various aspects of fertility predictionfrom wearable-based physiological data as described herein. For example,the wearable application 820 may include a data acquisition component825, a temperature data component 830, a cycle length component 835, afertility prediction component 840, a user interface component 845, orany combination thereof. Each of these components may communicate,directly or indirectly, with one another (e.g., via one or more buses).

The data acquisition component 825 may be configured as or otherwisesupport a means for receiving physiological data associated with a userfrom a wearable device, the physiological data comprising at leasttemperature data. The temperature data component 830 may be configuredas or otherwise support a means for determining a time series of aplurality of temperature values taken over a plurality of days based atleast in part on the received temperature data, wherein the time seriescomprises a plurality of menstrual cycles for the user. The cycle lengthcomponent 835 may be configured as or otherwise support a means fordetermining a plurality of menstrual cycle length parameters associatedwith the plurality of menstrual cycles based at least in part on thereceived temperature data. The fertility prediction component 840 may beconfigured as or otherwise support a means for determining a userfertility prediction based at least in part on determining the pluralityof menstrual cycle length parameters. The user interface component 845may be configured as or otherwise support a means for generating amessage for display on a graphical user interface on a user device thatindicates the determined user fertility prediction.

In some examples, the temperature data component 830 may be configuredas or otherwise support a means for identifying one or more localmaximum of the time series of the plurality of temperature values basedat least in part on determining the time series. In some examples, thetemperature data component 830 may be configured as or otherwise supporta means for calculating a duration between a first local maximum of theone or more local maximum and a second local maximum of the one or morelocal maximum based at least in part on the identifying, whereindetermining the plurality of menstrual cycle length parameters is basedat least in part on calculating the duration.

In some examples, the temperature data component 830 may be configuredas or otherwise support a means for identifying one or more positiveslopes of the time series of the plurality of temperature values basedat least in part on determining the time series, wherein determining theplurality of menstrual cycle length parameters is based at least in parton identifying the one or more positive slopes.

In some examples, the physiological data further comprises heart ratedata, and the data acquisition component 825 may be configured as orotherwise support a means for determining that the received heart ratedata satisfies a threshold heart rate for the user for at least aportion of the plurality of days, wherein determining the user fertilityprediction is based at least in part on determining that the receivedheart rate data satisfies the threshold heart rate for the user.

In some examples, the physiological data further comprises heart ratevariability data, and the data acquisition component 825 may beconfigured as or otherwise support a means for determining that thereceived heart rate variability data satisfies a threshold heart ratevariability for the user for at least a portion of the plurality ofdays, wherein determining the user fertility prediction is based atleast in part on determining that the received heart rate variabilitydata satisfies the threshold heart rate variability for the user.

In some examples, the physiological data further comprises respiratoryrate data, and the data acquisition component 825 may be configured asor otherwise support a means for determining that the respiratory ratedata satisfies a threshold respiratory rate for the user for at least aportion of the plurality of days, wherein determining the user fertilityprediction is based at least in part on determining that the receivedrespiratory rate data satisfies the threshold respiratory rate for theuser.

In some examples, the physiological data further comprises sleep data,and the data acquisition component 825 may be configured as or otherwisesupport a means for determining that a quantity of detected sleepdisturbances from the received sleep data satisfies a baseline sleepdisturbance threshold for the user for at least a portion of theplurality of days, wherein determining the user fertility prediction isbased at least in part on determining that the quantity of detectedsleep disturbances satisfies the baseline sleep disturbance thresholdfor the user.

In some examples, the plurality of menstrual cycle length parameterscomprise at least one of an average menstrual cycle length, a standarddeviation menstrual cycle length, an average follicular phase length, anaverage luteal phase length, a range of menstrual cycle lengths, aquantity of anovulatory cycles, or a combination thereof.

In some examples, the temperature data component 830 may be configuredas or otherwise support a means for determining each temperature valueof the plurality of temperature values based at least in part onreceiving the temperature data, wherein the temperature data comprisescontinuous daytime temperature data.

In some examples, the user interface component 845 may be configured asor otherwise support a means for receiving, via the user device, anindication comprising an age of the user, a weight of the user, alifestyle summary of the user, a type of fertility treatment experiencedby the user, a quantity of miscarriages experienced by the user, or acombination thereof, wherein determining the user fertility predictionis based at least in part on receiving the indication.

In some examples, the user interface component 845 may be configured asor otherwise support a means for transmitting the message that indicatesthe determined user fertility prediction to the user device, wherein theuser device is associated with a clinician, the user, a fertilityspecialist, or a combination thereof.

In some examples, the user interface component 845 may be configured asor otherwise support a means for causing a graphical user interface of auser device associated with the user to display a message associatedwith the determined user fertility prediction.

In some examples, the message further comprises a score associated withthe determined user fertility prediction, a time interval during whichthe determined user fertility prediction is valid for, a probability ofbecoming pregnant within the time interval based on the determined userfertility prediction, an indication of physiological and behavioralindicators that contribute to the determined user fertility prediction,educational content associated with the determined user fertilityprediction, recommendations to improve the determined user fertilityprediction, or a combination thereof.

In some examples, the fertility prediction component 840 may beconfigured as or otherwise support a means for inputting thephysiological data into a machine learning classifier, whereindetermining the user fertility prediction is based at least in part oninputting the physiological data into the machine learning classifier.

In some examples, the wearable device comprises a wearable ring device.

In some examples, the wearable device collects the physiological datafrom the user based on arterial blood flow.

FIG. 9 shows a diagram of a system 900 including a device 905 thatsupports fertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure. The device 905 may bean example of or include the components of a device 705 as describedherein. The device 905 may include an example of a user device 106, asdescribed previously herein. The device 905 may include components forbi-directional communications including components for transmitting andreceiving communications with a wearable device 104 and a server 110,such as a wearable application 920, a communication module 910, anantenna 915, a user interface component 925, a database (applicationdata) 930, a memory 935, and a processor 940. These components may be inelectronic communication or otherwise coupled (e.g., operatively,communicatively, functionally, electronically, electrically) via one ormore buses (e.g., a bus 945).

The communication module 910 may manage input and output signals for thedevice 905 via the antenna 915. The communication module 910 may includean example of the communication module 220-b of the user device 106shown and described in FIG. 2. In this regard, the communication module910 may manage communications with the ring 104 and the server 110, asillustrated in FIG. 2. The communication module 910 may also manageperipherals not integrated into the device 905. In some cases, thecommunication module 910 may represent a physical connection or port toan external peripheral. In some cases, the communication module 910 mayutilize an operating system such as iOS®, ANDROID®, MS-DOS®,MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system. Inother cases, the communication module 910 may represent or interact witha wearable device (e.g., ring 104), modem, a keyboard, a mouse, atouchscreen, or a similar device. In some cases, the communicationmodule 910 may be implemented as part of the processor 940. In someexamples, a user may interact with the device 905 via the communicationmodule 910, user interface component 925, or via hardware componentscontrolled by the communication module 910.

In some cases, the device 905 may include a single antenna 915. However,in some other cases, the device 905 may have more than one antenna 915,which may be capable of concurrently transmitting or receiving multiplewireless transmissions. The communication module 910 may communicatebi-directionally, via the one or more antennas 915, wired, or wirelesslinks as described herein. For example, the communication module 910 mayrepresent a wireless transceiver and may communicate bi-directionallywith another wireless transceiver. The communication module 910 may alsoinclude a modem to modulate the packets, to provide the modulatedpackets to one or more antennas 915 for transmission, and to demodulatepackets received from the one or more antennas 915.

The user interface component 925 may manage data storage and processingin a database 930. In some cases, a user may interact with the userinterface component 925. In other cases, the user interface component925 may operate automatically without user interaction. The database 930may be an example of a single database, a distributed database, multipledistributed databases, a data store, a data lake, or an emergency backupdatabase.

The memory 935 may include RAM and ROM. The memory 935 may storecomputer-readable, computer-executable software including instructionsthat, when executed, cause the processor 940 to perform variousfunctions described herein. In some cases, the memory 935 may contain,among other things, a BIOS which may control basic hardware or softwareoperation such as the interaction with peripheral components or devices.

The processor 940 may include an intelligent hardware device, (e.g., ageneral-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, anFPGA, a programmable logic device, a discrete gate or transistor logiccomponent, a discrete hardware component, or any combination thereof).In some cases, the processor 940 may be configured to operate a memoryarray using a memory controller. In other cases, a memory controller maybe integrated into the processor 940. The processor 940 may beconfigured to execute computer-readable instructions stored in a memory935 to perform various functions (e.g., functions or tasks supporting amethod and system for sleep staging algorithms).

For example, the wearable application 920 may be configured as orotherwise support a means for receiving physiological data associatedwith a user from a wearable device, the physiological data comprising atleast temperature data. The wearable application 920 may be configuredas or otherwise support a means for determining a time series of aplurality of temperature values taken over a plurality of days based atleast in part on the received temperature data, wherein the time seriescomprises a plurality of menstrual cycles for the user. The wearableapplication 920 may be configured as or otherwise support a means fordetermining a plurality of menstrual cycle length parameters associatedwith the plurality of menstrual cycles based at least in part on thereceived temperature data. The wearable application 920 may beconfigured as or otherwise support a means for determining a userfertility prediction based at least in part on determining the pluralityof menstrual cycle length parameters. The wearable application 920 maybe configured as or otherwise support a means for generating a messagefor display on a graphical user interface on a user device thatindicates the determined user fertility prediction.

By including or configuring the wearable application 920 in accordancewith examples as described herein, the device 905 may support techniquesfor improved communication reliability, reduced latency, improved userexperience related to reduced processing, reduced power consumption,more efficient utilization of communication resources, improvedcoordination between devices, longer battery life, improved utilizationof processing capability.

The wearable application 920 may include an application (e.g., “app”),program, software, or other component which is configured to facilitatecommunications with a ring 104, server 110, other user devices 106, andthe like. For example, the wearable application 920 may include anapplication executable on a user device 106 which is configured toreceive data (e.g., physiological data) from a ring 104, performprocessing operations on the received data, transmit and receive datawith the servers 110, and cause presentation of data to a user 102.

FIG. 10 shows a flowchart illustrating a method 1000 that supportsfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure. The operations of themethod 1000 may be implemented by a user device or its components asdescribed herein. For example, the operations of the method 1000 may beperformed by a user device as described with reference to FIGS. 1through 9. In some examples, a user device may execute a set ofinstructions to control the functional elements of the user device toperform the described functions. Additionally, or alternatively, theuser device may perform aspects of the described functions usingspecial-purpose hardware.

At 1005, the method may include receiving physiological data associatedwith a user from a wearable device, the physiological data comprising atleast temperature data. The operations of 1005 may be performed inaccordance with examples as disclosed herein. In some examples, aspectsof the operations of 1005 may be performed by a data acquisitioncomponent 825 as described with reference to FIG. 8.

At 1010, the method may include determining a time series of a pluralityof temperature values taken over a plurality of days based at least inpart on the received temperature data, wherein the time series comprisesa plurality of menstrual cycles for the user. The operations of 1010 maybe performed in accordance with examples as disclosed herein. In someexamples, aspects of the operations of 1010 may be performed by atemperature data component 830 as described with reference to FIG. 8.

At 1015, the method may include determining a plurality of menstrualcycle length parameters associated with the plurality of menstrualcycles based at least in part on the received temperature data. Theoperations of 1015 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1015may be performed by a cycle length component 835 as described withreference to FIG. 8.

At 1020, the method may include determining a user fertility predictionbased at least in part on determining the plurality of menstrual cyclelength parameters. The operations of 1020 may be performed in accordancewith examples as disclosed herein. In some examples, aspects of theoperations of 1020 may be performed by a fertility prediction component840 as described with reference to FIG. 8.

At 1025, the method may include generating a message for display on agraphical user interface on a user device that indicates the determineduser fertility prediction. The operations of 1025 may be performed inaccordance with examples as disclosed herein. In some examples, aspectsof the operations of 1025 may be performed by a user interface component845 as described with reference to FIG. 8.

FIG. 11 shows a flowchart illustrating a method 1100 that supportsfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure. The operations of themethod 1100 may be implemented by a user device or its components asdescribed herein. For example, the operations of the method 1100 may beperformed by a user device as described with reference to FIGS. 1through 9. In some examples, a user device may execute a set ofinstructions to control the functional elements of the user device toperform the described functions. Additionally, or alternatively, theuser device may perform aspects of the described functions usingspecial-purpose hardware.

At 1105, the method may include receiving physiological data associatedwith a user from a wearable device, the physiological data comprising atleast temperature data. The operations of 1105 may be performed inaccordance with examples as disclosed herein. In some examples, aspectsof the operations of 1105 may be performed by a data acquisitioncomponent 825 as described with reference to FIG. 8.

At 1110, the method may include determining a time series of a pluralityof temperature values taken over a plurality of days based at least inpart on the received temperature data, wherein the time series comprisesa plurality of menstrual cycles for the user. The operations of 1110 maybe performed in accordance with examples as disclosed herein. In someexamples, aspects of the operations of 1110 may be performed by atemperature data component 830 as described with reference to FIG. 8.

At 1115, the method may include determining a plurality of menstrualcycle length parameters associated with the plurality of menstrualcycles based at least in part on the received temperature data. Theoperations of 1115 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1115may be performed by a cycle length component 835 as described withreference to FIG. 8.

At 1120, the method may include receiving, via the user device, anindication comprising an age of the user, a weight of the user, alifestyle summary of the user, a type of fertility treatment experiencedby the user, a quantity of miscarriages experienced by the user, or acombination thereof, wherein determining the user fertility predictionis based at least in part on receiving the indication. The operations of1120 may be performed in accordance with examples as disclosed herein.In some examples, aspects of the operations of 1120 may be performed bya user interface component 845 as described with reference to FIG. 8.

At 1125, the method may include determining a user fertility predictionbased at least in part on determining the plurality of menstrual cyclelength parameters. The operations of 1125 may be performed in accordancewith examples as disclosed herein. In some examples, aspects of theoperations of 1125 may be performed by a fertility prediction component840 as described with reference to FIG. 8.

At 1130, the method may include generating a message for display on agraphical user interface on a user device that indicates the determineduser fertility prediction. The operations of 1130 may be performed inaccordance with examples as disclosed herein. In some examples, aspectsof the operations of 1130 may be performed by a user interface component845 as described with reference to FIG. 8.

FIG. 12 shows a flowchart illustrating a method 1200 that supportsfertility prediction from wearable-based physiological data inaccordance with aspects of the present disclosure. The operations of themethod 1200 may be implemented by a user device or its components asdescribed herein. For example, the operations of the method 1200 may beperformed by a user device as described with reference to FIGS. 1through 9. In some examples, a user device may execute a set ofinstructions to control the functional elements of the user device toperform the described functions. Additionally, or alternatively, theuser device may perform aspects of the described functions usingspecial-purpose hardware.

At 1205, the method may include receiving physiological data associatedwith a user from a wearable device, the physiological data comprising atleast temperature data. The operations of 1205 may be performed inaccordance with examples as disclosed herein. In some examples, aspectsof the operations of 1205 may be performed by a data acquisitioncomponent 825 as described with reference to FIG. 8.

At 1210, the method may include determining a time series of a pluralityof temperature values taken over a plurality of days based at least inpart on the received temperature data, wherein the time series comprisesa plurality of menstrual cycles for the user. The operations of 1210 maybe performed in accordance with examples as disclosed herein. In someexamples, aspects of the operations of 1210 may be performed by atemperature data component 830 as described with reference to FIG. 8.

At 1215, the method may include determining a plurality of menstrualcycle length parameters associated with the plurality of menstrualcycles based at least in part on the received temperature data. Theoperations of 1215 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1215may be performed by a cycle length component 835 as described withreference to FIG. 8.

At 1220, the method may include determining a user fertility predictionbased at least in part on determining the plurality of menstrual cyclelength parameters. The operations of 1220 may be performed in accordancewith examples as disclosed herein. In some examples, aspects of theoperations of 1220 may be performed by a fertility prediction component840 as described with reference to FIG. 8.

At 1225, the method may include generating a message for display on agraphical user interface on a user device that indicates the determineduser fertility prediction. The operations of 1225 may be performed inaccordance with examples as disclosed herein. In some examples, aspectsof the operations of 1225 may be performed by a user interface component845 as described with reference to FIG. 8.

At 1230, the method may include transmitting the message that indicatesthe determined user fertility prediction to the user device, wherein theuser device is associated with a clinician, the user, a fertilityspecialist, or a combination thereof. The operations of 1230 may beperformed in accordance with examples as disclosed herein. In someexamples, aspects of the operations of 1230 may be performed by a userinterface component 845 as described with reference to FIG. 8.

It should be noted that the methods described above describe possibleimplementations, and that the operations and the steps may be rearrangedor otherwise modified and that other implementations are possible.Furthermore, aspects from two or more of the methods may be combined.

A method is described. The method may include receiving physiologicaldata associated with a user from a wearable device, the physiologicaldata comprising at least temperature data, determining a time series ofa plurality of temperature values taken over a plurality of days basedat least in part on the received temperature data, wherein the timeseries comprises a plurality of menstrual cycles for the user,determining a plurality of menstrual cycle length parameters associatedwith the plurality of menstrual cycles based at least in part on thereceived temperature data, determining a user fertility prediction basedat least in part on determining the plurality of menstrual cycle lengthparameters, and generating a message for display on a graphical userinterface on a user device that indicates the determined user fertilityprediction.

An apparatus is described. The apparatus may include a processor, memorycoupled with the processor, and instructions stored in the memory. Theinstructions may be executable by the processor to cause the apparatusto receive physiological data associated with a user from a wearabledevice, the physiological data comprising at least temperature data,determine a time series of a plurality of temperature values taken overa plurality of days based at least in part on the received temperaturedata, wherein the time series comprises a plurality of menstrual cyclesfor the user, determine a plurality of menstrual cycle length parametersassociated with the plurality of menstrual cycles based at least in parton the received temperature data, determine a user fertility predictionbased at least in part on determining the plurality of menstrual cyclelength parameters, and generate a message for display on a graphicaluser interface on a user device that indicates the determined userfertility prediction.

Another apparatus is described. The apparatus may include means forreceiving physiological data associated with a user from a wearabledevice, the physiological data comprising at least temperature data,means for determining a time series of a plurality of temperature valuestaken over a plurality of days based at least in part on the receivedtemperature data, wherein the time series comprises a plurality ofmenstrual cycles for the user, means for determining a plurality ofmenstrual cycle length parameters associated with the plurality ofmenstrual cycles based at least in part on the received temperaturedata, means for determining a user fertility prediction based at leastin part on determining the plurality of menstrual cycle lengthparameters, and means for generating a message for display on agraphical user interface on a user device that indicates the determineduser fertility prediction.

A non-transitory computer-readable medium storing code is described. Thecode may include instructions executable by a processor to receivephysiological data associated with a user from a wearable device, thephysiological data comprising at least temperature data, determine atime series of a plurality of temperature values taken over a pluralityof days based at least in part on the received temperature data, whereinthe time series comprises a plurality of menstrual cycles for the user,determine a plurality of menstrual cycle length parameters associatedwith the plurality of menstrual cycles based at least in part on thereceived temperature data, determine a user fertility prediction basedat least in part on determining the plurality of menstrual cycle lengthparameters, and generate a message for display on a graphical userinterface on a user device that indicates the determined user fertilityprediction.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for identifying one or morelocal maximum of the time series of the plurality of temperature valuesbased at least in part on determining the time series and calculating aduration between a first local maximum of the one or more local maximumand a second local maximum of the one or more local maximum based atleast in part on the identifying, wherein determining the plurality ofmenstrual cycle length parameters may be based at least in part oncalculating the duration.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for identifying one or morepositive slopes of the time series of the plurality of temperaturevalues based at least in part on determining the time series, whereindetermining the plurality of menstrual cycle length parameters may bebased at least in part on identifying the one or more positive slopes.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the physiological datafurther comprises heart rate data and the method, apparatuses, andnon-transitory computer-readable medium may include further operations,features, means, or instructions for determining that the received heartrate data satisfies a threshold heart rate for the user for at least aportion of the plurality of days, wherein determining the user fertilityprediction may be based at least in part on determining that thereceived heart rate data satisfies the threshold heart rate for theuser.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the physiological datafurther comprises heart rate variability data and the method,apparatuses, and non-transitory computer-readable medium may includefurther operations, features, means, or instructions for determiningthat the received heart rate variability data satisfies a thresholdheart rate variability for the user for at least a portion of theplurality of days, wherein determining the user fertility prediction maybe based at least in part on determining that the received heart ratevariability data satisfies the threshold heart rate variability for theuser.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the physiological datafurther comprises respiratory rate data and the method, apparatuses, andnon-transitory computer-readable medium may include further operations,features, means, or instructions for determining that the respiratoryrate data satisfies a threshold respiratory rate for the user for atleast a portion of the plurality of days, wherein determining the userfertility prediction may be based at least in part on determining thatthe received respiratory rate data satisfies the threshold respiratoryrate for the user.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the physiological datafurther comprises sleep data and the method, apparatuses, andnon-transitory computer-readable medium may include further operations,features, means, or instructions for determining that a quantity ofdetected sleep disturbances from the received sleep data satisfies abaseline sleep disturbance threshold for the user for at least a portionof the plurality of days, wherein determining the user fertilityprediction may be based at least in part on determining that thequantity of detected sleep disturbances satisfies the baseline sleepdisturbance threshold for the user.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the plurality of menstrualcycle length parameters comprise at least one of an average menstrualcycle length, a standard deviation menstrual cycle length, an averagefollicular phase length, an average luteal phase length, a range ofmenstrual cycle lengths, a quantity of anovulatory cycles, or acombination thereof.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for determining eachtemperature value of the plurality of temperature values based at leastin part on receiving the temperature data, wherein the temperature datacomprises continuous daytime temperature data.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving, via the userdevice, an indication comprising an age of the user, a weight of theuser, a lifestyle summary of the user, a type of fertility treatmentexperienced by the user, a quantity of miscarriages experienced by theuser, or a combination thereof, wherein determining the user fertilityprediction may be based at least in part on receiving the indication.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for transmitting themessage that indicates the determined user fertility prediction to theuser device, wherein the user device may be associated with a clinician,the user, a fertility specialist, or a combination thereof.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for causing a graphicaluser interface of a user device associated with the user to display amessage associated with the determined user fertility prediction.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the message further comprisesa score associated with the determined user fertility prediction, a timeinterval during which the determined user fertility prediction may bevalid for, a probability of becoming pregnant within the time intervalbased on the determined user fertility prediction, an indication ofphysiological and behavioral indicators that contribute to thedetermined user fertility prediction, educational content associatedwith the determined user fertility prediction, recommendations toimprove the determined user fertility prediction, or a combinationthereof.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for inputting thephysiological data into a machine learning classifier, whereindetermining the user fertility prediction may be based at least in parton inputting the physiological data into the machine learningclassifier.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the wearable device comprisesa wearable ring device.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the wearable device collectsthe physiological data from the user based on arterial blood flow.

The description set forth herein, in connection with the appendeddrawings, describes example configurations and does not represent allthe examples that may be implemented or that are within the scope of theclaims. The term “exemplary” used herein means “serving as an example,instance, or illustration,” and not “preferred” or “advantageous overother examples.” The detailed description includes specific details forthe purpose of providing an understanding of the described techniques.These techniques, however, may be practiced without these specificdetails. In some instances, well-known structures and devices are shownin block diagram form in order to avoid obscuring the concepts of thedescribed examples.

In the appended figures, similar components or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If just the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

Information and signals described herein may be represented using any ofa variety of different technologies and techniques. For example, data,instructions, commands, information, signals, bits, symbols, and chipsthat may be referenced throughout the above description may berepresented by voltages, currents, electromagnetic waves, magneticfields or particles, optical fields or particles, or any combinationthereof.

The various illustrative blocks and modules described in connection withthe disclosure herein may be implemented or performed with ageneral-purpose processor, a DSP, an ASIC, an FPGA or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general-purpose processor may be a microprocessor,but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices (e.g., a combinationof a DSP and a microprocessor, multiple microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration).

The functions described herein may be implemented in hardware, softwareexecuted by a processor, firmware, or any combination thereof. Ifimplemented in software executed by a processor, the functions may bestored on or transmitted over as one or more instructions or code on acomputer-readable medium. Other examples and implementations are withinthe scope of the disclosure and appended claims. For example, due to thenature of software, functions described above can be implemented usingsoftware executed by a processor, hardware, firmware, hardwiring, orcombinations of any of these. Features implementing functions may alsobe physically located at various positions, including being distributedsuch that portions of functions are implemented at different physicallocations. Also, as used herein, including in the claims, “or” as usedin a list of items (for example, a list of items prefaced by a phrasesuch as “at least one of” or “one or more of”) indicates an inclusivelist such that, for example, a list of at least one of A, B, or C meansA or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, asused herein, the phrase “based on” shall not be construed as a referenceto a closed set of conditions. For example, an exemplary step that isdescribed as “based on condition A” may be based on both a condition Aand a condition B without departing from the scope of the presentdisclosure. In other words, as used herein, the phrase “based on” shallbe construed in the same manner as the phrase “based at least in parton.”

Computer-readable media includes both non-transitory computer storagemedia and communication media including any medium that facilitatestransfer of a computer program from one place to another. Anon-transitory storage medium may be any available medium that can beaccessed by a general purpose or special purpose computer. By way ofexample, and not limitation, non-transitory computer-readable media cancomprise RAM, ROM, electrically erasable programmable ROM (EEPROM),compact disk (CD) ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other non-transitorymedium that can be used to carry or store desired program code means inthe form of instructions or data structures and that can be accessed bya general-purpose or special-purpose computer, or a general-purpose orspecial-purpose processor. Also, any connection is properly termed acomputer-readable medium. For example, if the software is transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared, radio, and microwave, then thecoaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. Disk and disc, as used herein, include CD, laserdisc, optical disc, digital versatile disc (DVD), floppy disk andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveare also included within the scope of computer-readable media.

The description herein is provided to enable a person skilled in the artto make or use the disclosure. Various modifications to the disclosurewill be readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other variations withoutdeparting from the scope of the disclosure. Thus, the disclosure is notlimited to the examples and designs described herein, but is to beaccorded the broadest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A method comprising: receiving physiological dataassociated with a user from a wearable device, the physiological datacomprising at least temperature data; determining a time series of aplurality of temperature values taken over a plurality of days based atleast in part on the received temperature data, wherein the time seriescomprises a plurality of menstrual cycles for the user; determining aplurality of menstrual cycle length parameters associated with theplurality of menstrual cycles based at least in part on the receivedtemperature data; determining a user fertility prediction based at leastin part on determining the plurality of menstrual cycle lengthparameters; and generating a message for display on a graphical userinterface on a user device that indicates the determined user fertilityprediction.
 2. The method of claim 1, further comprising: identifyingone or more local maximum of the time series of the plurality oftemperature values based at least in part on determining the timeseries; and calculating a duration between a first local maximum of theone or more local maximum and a second local maximum of the one or morelocal maximum based at least in part on the identifying, whereindetermining the plurality of menstrual cycle length parameters is basedat least in part on calculating the duration.
 3. The method of claim 1,further comprising: identifying one or more positive slopes of the timeseries of the plurality of temperature values based at least in part ondetermining the time series, wherein determining the plurality ofmenstrual cycle length parameters is based at least in part onidentifying the one or more positive slopes.
 4. The method of claim 1,wherein the physiological data further comprises heart rate data, themethod further comprising: determining that the received heart rate datasatisfies a threshold heart rate for the user for at least a portion ofthe plurality of days, wherein determining the user fertility predictionis based at least in part on determining that the received heart ratedata satisfies the threshold heart rate for the user.
 5. The method ofclaim 1, wherein the physiological data further comprises heart ratevariability data, the method further comprising: determining that thereceived heart rate variability data satisfies a threshold heart ratevariability for the user for at least a portion of the plurality ofdays, wherein determining the user fertility prediction is based atleast in part on determining that the received heart rate variabilitydata satisfies the threshold heart rate variability for the user.
 6. Themethod of claim 1, wherein the physiological data further comprisesrespiratory rate data, the method further comprising: determining thatthe respiratory rate data satisfies a threshold respiratory rate for theuser for at least a portion of the plurality of days, whereindetermining the user fertility prediction is based at least in part ondetermining that the received respiratory rate data satisfies thethreshold respiratory rate for the user.
 7. The method of claim 1,wherein the physiological data further comprises sleep data, the methodfurther comprising: determining that a quantity of detected sleepdisturbances from the received sleep data satisfies a baseline sleepdisturbance threshold for the user for at least a portion of theplurality of days, wherein determining the user fertility prediction isbased at least in part on determining that the quantity of detectedsleep disturbances satisfies the baseline sleep disturbance thresholdfor the user.
 8. The method of claim 1, wherein the plurality ofmenstrual cycle length parameters comprise at least one of an averagemenstrual cycle length, a standard deviation menstrual cycle length, anaverage follicular phase length, an average luteal phase length, a rangeof menstrual cycle lengths, a quantity of anovulatory cycles, or acombination thereof.
 9. The method of claim 1, further comprising:determining each temperature value of the plurality of temperaturevalues based at least in part on receiving the temperature data, whereinthe temperature data comprises continuous daytime temperature data. 10.The method of claim 1, further comprising: receiving, via the userdevice, an indication comprising an age of the user, a weight of theuser, a lifestyle summary of the user, a type of fertility treatmentexperienced by the user, a quantity of miscarriages experienced by theuser, or a combination thereof, wherein determining the user fertilityprediction is based at least in part on receiving the indication. 11.The method of claim 1, further comprising: transmitting the message thatindicates the determined user fertility prediction to the user device,wherein the user device is associated with a clinician, the user, afertility specialist, or a combination thereof.
 12. The method of claim1, further comprising: causing a graphical user interface of a userdevice associated with the user to display a message associated with thedetermined user fertility prediction.
 13. The method of claim 12,wherein the message further comprises a score associated with thedetermined user fertility prediction, a time interval during which thedetermined user fertility prediction is valid for, a probability ofbecoming pregnant within the time interval based on the determined userfertility prediction, an indication of physiological and behavioralindicators that contribute to the determined user fertility prediction,educational content associated with the determined user fertilityprediction, recommendations to improve the determined user fertilityprediction, or a combination thereof.
 14. The method of claim 1, furthercomprising: inputting the physiological data into a machine learningclassifier, wherein determining the user fertility prediction is basedat least in part on inputting the physiological data into the machinelearning classifier.
 15. The method of claim 1, wherein the wearabledevice comprises a wearable ring device.
 16. The method of claim 1,wherein the wearable device collects the physiological data from theuser based on arterial blood flow.
 17. An apparatus, comprising: aprocessor; memory coupled with the processor; and instructions stored inthe memory and executable by the processor to cause the apparatus to:receive physiological data associated with a user from a wearabledevice, the physiological data comprising at least temperature data;determine a time series of a plurality of temperature values taken overa plurality of days based at least in part on the received temperaturedata, wherein the time series comprises a plurality of menstrual cyclesfor the user; determine a plurality of menstrual cycle length parametersassociated with the plurality of menstrual cycles based at least in parton the received temperature data; determine a user fertility predictionbased at least in part on determining the plurality of menstrual cyclelength parameters; and generate a message for display on a graphicaluser interface on a user device that indicates the determined userfertility prediction.
 18. The apparatus of claim 17, wherein theinstructions are further executable by the processor to cause theapparatus to: identify one or more local maximum of the time series ofthe plurality of temperature values based at least in part ondetermining the time series; and calculate a duration between a firstlocal maximum of the one or more local maximum and a second localmaximum of the one or more local maximum based at least in part on theidentifying, wherein determining the plurality of menstrual cycle lengthparameters is based at least in part on calculating the duration.
 19. Anon-transitory computer-readable medium storing code, the codecomprising instructions executable by a processor to: receivephysiological data associated with a user from a wearable device, thephysiological data comprising at least temperature data; determine atime series of a plurality of temperature values taken over a pluralityof days based at least in part on the received temperature data, whereinthe time series comprises a plurality of menstrual cycles for the user;determine a plurality of menstrual cycle length parameters associatedwith the plurality of menstrual cycles based at least in part on thereceived temperature data; determine a user fertility prediction basedat least in part on determining the plurality of menstrual cycle lengthparameters; and generate a message for display on a graphical userinterface on a user device that indicates the determined user fertilityprediction.
 20. The non-transitory computer-readable medium of claim 19,wherein the instructions are further executable by the processor to:identify one or more local maximum of the time series of the pluralityof temperature values based at least in part on determining the timeseries; and calculate a duration between a first local maximum of theone or more local maximum and a second local maximum of the one or morelocal maximum based at least in part on the identifying, whereindetermining the plurality of menstrual cycle length parameters is basedat least in part on calculating the duration.